A Hospitalist Is Born

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The hospitalist business model has been adopted nationwide to help stem the tide of obstetrician/gynecologists who forgo delivering babies at hospitals—or providing emergency obstetric care—because of skyrocketing malpractice costs and a frustration with on-call schedules. Like the familiar HM model, a new class of OB-GYN has cropped up to work in medical centers.

Called laborists or OB hospitalists, this breed resembles hospital-based physicians: They work out of their respective institutions but don’t have private-practice patients. Hospitals like the arrangement because they have trained delivery staff in-house, and private-practice physicians don't mind giving up hospital calls because they can earn more cycling patients through their office.

The OB Hospitalist Group of Greenville, S.C., already has placed 60 OB hospitalists in six states, including Texas, California, and Florida. Group president Chris Swain, MD, hopes to place 100 by year's end, but only if he can find enough qualified candidates to manage both deliveries and OB emergencies.

"If you're going to be in a hospital 24 hours a day, you want to be able to handle all the emergencies that come in," Dr. Swain says. “You can't take time to look it up. You need to know it."

Dr. Swain's group pays for that knowledge. The average OB-GYN makes about $280,000 per year; Dr. Swain's OB hospitalists earn roughly $300,000 a year. In exchange for the higher salaries and defined work schedules, OB Hospitalist Group requires board certification, monitoring courses, and continued training. "We want our doctors to be the experts in emergency obstetrics care," Dr. Swain says. "We pay our doctors more; we expect more out of them."

 

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The hospitalist business model has been adopted nationwide to help stem the tide of obstetrician/gynecologists who forgo delivering babies at hospitals—or providing emergency obstetric care—because of skyrocketing malpractice costs and a frustration with on-call schedules. Like the familiar HM model, a new class of OB-GYN has cropped up to work in medical centers.

Called laborists or OB hospitalists, this breed resembles hospital-based physicians: They work out of their respective institutions but don’t have private-practice patients. Hospitals like the arrangement because they have trained delivery staff in-house, and private-practice physicians don't mind giving up hospital calls because they can earn more cycling patients through their office.

The OB Hospitalist Group of Greenville, S.C., already has placed 60 OB hospitalists in six states, including Texas, California, and Florida. Group president Chris Swain, MD, hopes to place 100 by year's end, but only if he can find enough qualified candidates to manage both deliveries and OB emergencies.

"If you're going to be in a hospital 24 hours a day, you want to be able to handle all the emergencies that come in," Dr. Swain says. “You can't take time to look it up. You need to know it."

Dr. Swain's group pays for that knowledge. The average OB-GYN makes about $280,000 per year; Dr. Swain's OB hospitalists earn roughly $300,000 a year. In exchange for the higher salaries and defined work schedules, OB Hospitalist Group requires board certification, monitoring courses, and continued training. "We want our doctors to be the experts in emergency obstetrics care," Dr. Swain says. "We pay our doctors more; we expect more out of them."

 

The hospitalist business model has been adopted nationwide to help stem the tide of obstetrician/gynecologists who forgo delivering babies at hospitals—or providing emergency obstetric care—because of skyrocketing malpractice costs and a frustration with on-call schedules. Like the familiar HM model, a new class of OB-GYN has cropped up to work in medical centers.

Called laborists or OB hospitalists, this breed resembles hospital-based physicians: They work out of their respective institutions but don’t have private-practice patients. Hospitals like the arrangement because they have trained delivery staff in-house, and private-practice physicians don't mind giving up hospital calls because they can earn more cycling patients through their office.

The OB Hospitalist Group of Greenville, S.C., already has placed 60 OB hospitalists in six states, including Texas, California, and Florida. Group president Chris Swain, MD, hopes to place 100 by year's end, but only if he can find enough qualified candidates to manage both deliveries and OB emergencies.

"If you're going to be in a hospital 24 hours a day, you want to be able to handle all the emergencies that come in," Dr. Swain says. “You can't take time to look it up. You need to know it."

Dr. Swain's group pays for that knowledge. The average OB-GYN makes about $280,000 per year; Dr. Swain's OB hospitalists earn roughly $300,000 a year. In exchange for the higher salaries and defined work schedules, OB Hospitalist Group requires board certification, monitoring courses, and continued training. "We want our doctors to be the experts in emergency obstetrics care," Dr. Swain says. "We pay our doctors more; we expect more out of them."

 

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In the Literature: The Latest Research You Need to Know

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Clinical question: Does intensive glucose control reduce mortality at 90 days in adult ICU patients?

Background: The American Diabetic Association currently recommends tight glucose control for patients admitted to an ICU, despite conflicting evidence in the literature about the benefits of this practice.

Study design: Randomized controlled trial.

Setting: Medical and surgical ICUs at 42 hospitals in Australia, Canada, and New Zealand.

Synopsis: More than 6,000 medical and surgical ICU patients were randomly assigned to receive either intensive (target blood sugar range of 81 mg/dL to 108 mg/dL) or conventional (target blood sugar of <180 mg/dL) glucose control. Eligible patients were expected to stay at least three days in the ICU. Mortality at 90 days for the intensive treatment group was 27.5% versus 24.9% in the conventional treatment group, with an absolute difference in mortality of 2.6% resulting in a number needed to harm of 38.5. This difference in mortality remained significant whether the patients were in a SICU or MICU, diabetic or nondiabetic, with or without sepsis, and with APACHE II scores of above or below 25. The excess deaths were attributed to cardiovascular causes, but more investigation is needed. This study demonstrates that there is no additional benefit and that there may be harm in pursuing aggressive glucose control in ICU patients.

Bottom line: When compared with conventional practice in adult ICU patients, intensive glucose control resulted in an increase in 90-day mortality.

Citation: NICE-SUGAR Study Investigators, Finfer S, Chittock DR, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283-1297.

 

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Clinical question: Does intensive glucose control reduce mortality at 90 days in adult ICU patients?

Background: The American Diabetic Association currently recommends tight glucose control for patients admitted to an ICU, despite conflicting evidence in the literature about the benefits of this practice.

Study design: Randomized controlled trial.

Setting: Medical and surgical ICUs at 42 hospitals in Australia, Canada, and New Zealand.

Synopsis: More than 6,000 medical and surgical ICU patients were randomly assigned to receive either intensive (target blood sugar range of 81 mg/dL to 108 mg/dL) or conventional (target blood sugar of <180 mg/dL) glucose control. Eligible patients were expected to stay at least three days in the ICU. Mortality at 90 days for the intensive treatment group was 27.5% versus 24.9% in the conventional treatment group, with an absolute difference in mortality of 2.6% resulting in a number needed to harm of 38.5. This difference in mortality remained significant whether the patients were in a SICU or MICU, diabetic or nondiabetic, with or without sepsis, and with APACHE II scores of above or below 25. The excess deaths were attributed to cardiovascular causes, but more investigation is needed. This study demonstrates that there is no additional benefit and that there may be harm in pursuing aggressive glucose control in ICU patients.

Bottom line: When compared with conventional practice in adult ICU patients, intensive glucose control resulted in an increase in 90-day mortality.

Citation: NICE-SUGAR Study Investigators, Finfer S, Chittock DR, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283-1297.

 

Clinical question: Does intensive glucose control reduce mortality at 90 days in adult ICU patients?

Background: The American Diabetic Association currently recommends tight glucose control for patients admitted to an ICU, despite conflicting evidence in the literature about the benefits of this practice.

Study design: Randomized controlled trial.

Setting: Medical and surgical ICUs at 42 hospitals in Australia, Canada, and New Zealand.

Synopsis: More than 6,000 medical and surgical ICU patients were randomly assigned to receive either intensive (target blood sugar range of 81 mg/dL to 108 mg/dL) or conventional (target blood sugar of <180 mg/dL) glucose control. Eligible patients were expected to stay at least three days in the ICU. Mortality at 90 days for the intensive treatment group was 27.5% versus 24.9% in the conventional treatment group, with an absolute difference in mortality of 2.6% resulting in a number needed to harm of 38.5. This difference in mortality remained significant whether the patients were in a SICU or MICU, diabetic or nondiabetic, with or without sepsis, and with APACHE II scores of above or below 25. The excess deaths were attributed to cardiovascular causes, but more investigation is needed. This study demonstrates that there is no additional benefit and that there may be harm in pursuing aggressive glucose control in ICU patients.

Bottom line: When compared with conventional practice in adult ICU patients, intensive glucose control resulted in an increase in 90-day mortality.

Citation: NICE-SUGAR Study Investigators, Finfer S, Chittock DR, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283-1297.

 

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Team Approach to Patient Care

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Hospitalists find themselves working with nonphysician providers (NPP) more and more as institutions spread workloads and cut costs. The work arrangement can be panaceas when they work well, barely palatable when they don’t.

Several sessions at HM09 in Chicago explored the best practices of HM groups that use nurse practitioners (NPs) and physician assistants (PAs). A particularly popular session examined case studies that showed two main trends: NPP programs run into trouble when the people involved view their positions as competition; programs succeed when there is buy-in from all stakeholders.

“There are things DOs and MDs do better than NPs and PAs, and there are things NPs and PAs do better than DOs and MDs,” says Mitch Wilson, MD, FHM, corporate medical director for Eagle Hospital Physicians in Atlanta. "It’s all about understanding the cumulative skill set.”

Dr. Wilson and Tracy Cardin, ACNP, at the University of Chicago Medical Center, used care stories as teaching tools for hospitals looking to implement new programs. The first cautionary tale failed because an “old school” culture left a new NPP so isolated from physicians and nursing staff that she quit. Two other cases showed NPPs integrate more easily because of mutual respect, defined responsibilities, and an alignment of expectations between both hospitalists and NPPs.

Mac McCormick, MD, vice president of clinical services at Eagle Hospital Physicians in Atlanta, offers the following suggestions to best utilize NPPs:

Conduct an analysis of your practice environment, bylaws, staff experience, and pre-existing attitudes to identify potential barriers and optimal opportunities in hiring NPPs;

Avoid pigeonholing NPPs into such narrow roles as completing discharge summaries or collecting data. Tasks that tend not to utilize their skills set might lead to professional dissatisfaction and likely aren't the most cost-efficient use of resources; and

Approach things in a team model. Shared visits are one way to accomplish this. Keep communication lines open to make sure accurate and timely information is available to all.

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Hospitalists find themselves working with nonphysician providers (NPP) more and more as institutions spread workloads and cut costs. The work arrangement can be panaceas when they work well, barely palatable when they don’t.

Several sessions at HM09 in Chicago explored the best practices of HM groups that use nurse practitioners (NPs) and physician assistants (PAs). A particularly popular session examined case studies that showed two main trends: NPP programs run into trouble when the people involved view their positions as competition; programs succeed when there is buy-in from all stakeholders.

“There are things DOs and MDs do better than NPs and PAs, and there are things NPs and PAs do better than DOs and MDs,” says Mitch Wilson, MD, FHM, corporate medical director for Eagle Hospital Physicians in Atlanta. "It’s all about understanding the cumulative skill set.”

Dr. Wilson and Tracy Cardin, ACNP, at the University of Chicago Medical Center, used care stories as teaching tools for hospitals looking to implement new programs. The first cautionary tale failed because an “old school” culture left a new NPP so isolated from physicians and nursing staff that she quit. Two other cases showed NPPs integrate more easily because of mutual respect, defined responsibilities, and an alignment of expectations between both hospitalists and NPPs.

Mac McCormick, MD, vice president of clinical services at Eagle Hospital Physicians in Atlanta, offers the following suggestions to best utilize NPPs:

Conduct an analysis of your practice environment, bylaws, staff experience, and pre-existing attitudes to identify potential barriers and optimal opportunities in hiring NPPs;

Avoid pigeonholing NPPs into such narrow roles as completing discharge summaries or collecting data. Tasks that tend not to utilize their skills set might lead to professional dissatisfaction and likely aren't the most cost-efficient use of resources; and

Approach things in a team model. Shared visits are one way to accomplish this. Keep communication lines open to make sure accurate and timely information is available to all.

Hospitalists find themselves working with nonphysician providers (NPP) more and more as institutions spread workloads and cut costs. The work arrangement can be panaceas when they work well, barely palatable when they don’t.

Several sessions at HM09 in Chicago explored the best practices of HM groups that use nurse practitioners (NPs) and physician assistants (PAs). A particularly popular session examined case studies that showed two main trends: NPP programs run into trouble when the people involved view their positions as competition; programs succeed when there is buy-in from all stakeholders.

“There are things DOs and MDs do better than NPs and PAs, and there are things NPs and PAs do better than DOs and MDs,” says Mitch Wilson, MD, FHM, corporate medical director for Eagle Hospital Physicians in Atlanta. "It’s all about understanding the cumulative skill set.”

Dr. Wilson and Tracy Cardin, ACNP, at the University of Chicago Medical Center, used care stories as teaching tools for hospitals looking to implement new programs. The first cautionary tale failed because an “old school” culture left a new NPP so isolated from physicians and nursing staff that she quit. Two other cases showed NPPs integrate more easily because of mutual respect, defined responsibilities, and an alignment of expectations between both hospitalists and NPPs.

Mac McCormick, MD, vice president of clinical services at Eagle Hospital Physicians in Atlanta, offers the following suggestions to best utilize NPPs:

Conduct an analysis of your practice environment, bylaws, staff experience, and pre-existing attitudes to identify potential barriers and optimal opportunities in hiring NPPs;

Avoid pigeonholing NPPs into such narrow roles as completing discharge summaries or collecting data. Tasks that tend not to utilize their skills set might lead to professional dissatisfaction and likely aren't the most cost-efficient use of resources; and

Approach things in a team model. Shared visits are one way to accomplish this. Keep communication lines open to make sure accurate and timely information is available to all.

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Tackle Medical School Debt

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Overwhelmed by medical school debt? You're not alone. A 2008 American Medical Association survey showed that the average 2007 medical school graduate was left with a $139,000 debt burden and a powerful incentive to avoid primary care.

But according to Renee Zerehi, the American College of Physicians' manager of health policy, increasing numbers of students choose HM because of flexible scheduling and opportunities to reduce their debt. Zerehi and Bijo Chacko, MD, FHM, a member of SHM's Young Physicians Committee and hospitalist program medical director for Preferred Health Partners in New York City, offer these strategies for debt reduction.

Understand your debt portfolio: Talk to a financial consultant to assess debt, your family situation, and lifestyle issues. "A strong, keen understanding of how debt impacts your budget is essential," Dr. Chacko says. Medical school loans often come with different interest rates and grace periods, so try to pay off the high-interest loans immediately, he explains.

Consolidate your debt: Loans from different lenders with different balances, interest rates, and due dates may best be handled by a federal consolidation loan. The AMA explains it all in

"The Ins and Outs of Student Loan Consolidation."

Student loan forgiveness: A number of hospitalist programs offer loan repayment programs. The National Health Service Corps, the Health Professions Scholarship Program, and state loan repayment programs offer loan forgiveness for physicians practicing in underserved areas. Visit the AAMC Web site for a comprehensive list.

NIH Faculty Loan Forgiveness: For academic hospitalists doing research, the National Institutes of Health (NIH) offers up to $35,000 a year for loan repayment and tax reimbursement for each year of service.

For more information, visit SHM's Young Physician microsite.

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Overwhelmed by medical school debt? You're not alone. A 2008 American Medical Association survey showed that the average 2007 medical school graduate was left with a $139,000 debt burden and a powerful incentive to avoid primary care.

But according to Renee Zerehi, the American College of Physicians' manager of health policy, increasing numbers of students choose HM because of flexible scheduling and opportunities to reduce their debt. Zerehi and Bijo Chacko, MD, FHM, a member of SHM's Young Physicians Committee and hospitalist program medical director for Preferred Health Partners in New York City, offer these strategies for debt reduction.

Understand your debt portfolio: Talk to a financial consultant to assess debt, your family situation, and lifestyle issues. "A strong, keen understanding of how debt impacts your budget is essential," Dr. Chacko says. Medical school loans often come with different interest rates and grace periods, so try to pay off the high-interest loans immediately, he explains.

Consolidate your debt: Loans from different lenders with different balances, interest rates, and due dates may best be handled by a federal consolidation loan. The AMA explains it all in

"The Ins and Outs of Student Loan Consolidation."

Student loan forgiveness: A number of hospitalist programs offer loan repayment programs. The National Health Service Corps, the Health Professions Scholarship Program, and state loan repayment programs offer loan forgiveness for physicians practicing in underserved areas. Visit the AAMC Web site for a comprehensive list.

NIH Faculty Loan Forgiveness: For academic hospitalists doing research, the National Institutes of Health (NIH) offers up to $35,000 a year for loan repayment and tax reimbursement for each year of service.

For more information, visit SHM's Young Physician microsite.

Overwhelmed by medical school debt? You're not alone. A 2008 American Medical Association survey showed that the average 2007 medical school graduate was left with a $139,000 debt burden and a powerful incentive to avoid primary care.

But according to Renee Zerehi, the American College of Physicians' manager of health policy, increasing numbers of students choose HM because of flexible scheduling and opportunities to reduce their debt. Zerehi and Bijo Chacko, MD, FHM, a member of SHM's Young Physicians Committee and hospitalist program medical director for Preferred Health Partners in New York City, offer these strategies for debt reduction.

Understand your debt portfolio: Talk to a financial consultant to assess debt, your family situation, and lifestyle issues. "A strong, keen understanding of how debt impacts your budget is essential," Dr. Chacko says. Medical school loans often come with different interest rates and grace periods, so try to pay off the high-interest loans immediately, he explains.

Consolidate your debt: Loans from different lenders with different balances, interest rates, and due dates may best be handled by a federal consolidation loan. The AMA explains it all in

"The Ins and Outs of Student Loan Consolidation."

Student loan forgiveness: A number of hospitalist programs offer loan repayment programs. The National Health Service Corps, the Health Professions Scholarship Program, and state loan repayment programs offer loan forgiveness for physicians practicing in underserved areas. Visit the AAMC Web site for a comprehensive list.

NIH Faculty Loan Forgiveness: For academic hospitalists doing research, the National Institutes of Health (NIH) offers up to $35,000 a year for loan repayment and tax reimbursement for each year of service.

For more information, visit SHM's Young Physician microsite.

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Quality Care for COPD, Secondary Stroke Prevention, Treat Classic CTA

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Factors Influencing the Treatment of COPD

Lindenauer PK, Pekow P, Gao S, et al. Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2006;144:894-903.

Background

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, resulting in more than $18 billion in annual health costs. Acute exacerbations of COPD can lead to respiratory compromise and are one of the 10 leading causes of hospitalization in the United States.

Hospitalists currently have evidence-based guidelines available that recommend therapies for patients with acute exacerbations of COPD. This study was designed to evaluate the practice patterns in the United States and to evaluate the quality of care provided to hospitalized patients based on comparisons with these published guidelines. The authors did not report any conflicts of interest, and this work was performed without external grant support.

Methods

Using administrative data from the 360 hospitals that participate in Perspective, a database developed for measuring healthcare quality and utilization, the authors performed a retrospective cohort study. Patients hospitalized for a primary diagnosis of acute exacerbation of COPD were chosen. Patients with pneumonia were specifically excluded. The outcomes of interest included adherence to the diagnostic and therapeutic recommendations of the joint American College of Physicians and American College of Chest Physicians evidence-based COPD guideline, published in 2001.

Results

Of the 69,820 patients included in the analysis, 33% received “ideal care,” defined as all of the recommended care and none of the non-beneficial interventions. Specific results included varied utilization of recommended care: 95% had chest radiography, 91% received supplemental oxygen, 97% had bronchodilators, 85% were given systemic steroids, and 85% received antibiotics.

Overall, 45% of patients received at least one non-beneficial intervention specified in the guidelines: 24% were treated with methylxanthines, 14% underwent sputum testing, 12% had acute spirometry, 6% received chest physiotherapy, and 2% were given mucolytics.

Older patients and women were more likely to receive ideal care as defined, but hospitals with a higher annual volume of COPD cases were not associated with improved performance in this analysis.

Conclusions

Given a widely accepted evidence-based practice guideline as a benchmark, significant variation exists across hospitals in the quality of care for acute exacerbations of COPD. Opportunities exist to improve the quality of care, in particular by increasing the use of systemic corticosteroids and antibiotic therapy and reducing the utilization of many diagnostic and therapeutic interventions that are not only not recommended but are also potentially harmful.

Commentary

COPD management in the acute inpatient setting is on the horizon as a focus of policymakers, and this study suggests that significant opportunities exist for inpatient physicians to reduce variation in practice and utilize an evidence-based approach to the treatment of acute exacerbations of COPD. This study is limited by its use of administrative data, its inability to use clinical data to best determine appropriate care processes for individual patients, and its retrospective design.

As we move toward external quality metrics for the care of patients with acute exacerbations of COPD, further prospective studies evaluating clinical outcomes of interest, including mortality and readmission rates, are needed to determine the effects of adherence to ideal or recommended care for acute exacerbations of COPD.1-3

References

  1. Snow V, Lascher S, Mottur-Pilson C, et al. Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2001 Apr 3;134(7):595-599.
  2. American Thoracic Society. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, November 1986. Am Rev Respir Dis. 1987 Jul;136(1):225-244.
  3. Agency for Healthcare Research and Quality. Management of Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Rockville, Md.: Agency for Healthcare Research and Quality; 2000.
 

 

The Role of Dipyridamole in the Secondary Prevention of Stroke

ESPRIT Study Group. Aspirin plus dipyridamole versus aspirin alone after cerebral ischaemia of arterial origin (ESPRIT): randomized controlled trial. Lancet. 2006 May 20;367(9623):1665-1673.

Background

To date, studies have resulted in inconsistent results in trials of aspirin versus aspirin in combination with dipyridamole for secondary prevention of ischemic stroke. Four early, smaller studies have yielded non-significant results, in contrast to the statistically significant relative risk reduction seen with the addition of dipyridamole to aspirin in the European Stroke Prevention Study 2 (ESPS-2).1-2

Methods

The European/Australian Stroke Prevention in Reversible Ischaemia Trial (ESPRIT) study group conducted a prospective randomized controlled trial of 2,763 patients with transient ischemic attacks or minor ischemic stroke of presumed arterial origin who received aspirin (30-325 mg daily) with or without dipyridamole (200 mg twice daily) as secondary prevention. The primary outcome for this study was a composite of death from vascular causes, nonfatal stroke, nonfatal myocardial infarction, or major bleeding complication. Mean follow-up of patients enrolled was 3.5 years.

Results

In an intention-to-treat analysis, the primary combined endpoint occurred in 16% (216) of the patients on aspirin alone (median aspirin dose was 75 mg in both groups) compared with 13% (173) of the patients on aspirin plus dipyridamole. This result was statistically significant, with an absolute risk reduction of 1% per year. As noted in other trials, patients on dipyridamole discontinued their study medication more frequently than patients on aspirin alone, mostly due to headache.

Conclusions

The results of this trial, taken in the context of previously published data, support the combination of aspirin plus dipyridamole over aspirin alone for the secondary prevention of ischemic stroke of presumed arterial origin. Addition of these data to the previous meta-analysis of trials resulted in a statistically significant risk ratio for the composite endpoint of 0.82 (95% confidence interval, 0.74-0.91).1

Commentary

Ischemic stroke and transient ischemic attacks remain a challenge to effectively manage medically and are appropriately greatly feared health complications for many patients, resulting in significant morbidity and mortality. Prior studies of secondary prevention with aspirin therapy have demonstrated only a modest reduction in vascular complications in these patients.3-4

The results of this trial are consistent with data from the Second European Stroke Prevention Study, and in combination, these data confirm that the addition of dipyridamole for patients who can tolerate it offers significant benefit.2 The magnitude of the effect results in a number needed to treat of 100 patients for one year to prevent one vascular death, stroke, or myocardial infarction. Given the clinical significance of these outcomes, many patients may prefer a trial on combination therapy.

References

  1. Antithrombotic Trialists’ Collaboration. Collaborative meta-analysis of randomised trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients. BMJ. 2002 Jan 12;324(7329):71-86.
  2. Diener HC, Cunha L, Forbes C, et al. European Stroke Prevention Study. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke. J Neurol Sci. 1996;143:1-13.
  3. Warlow C. Secondary prevention of stroke. Lancet. 1992;339:724-727.
  4. Algra A, van Gijn J. Cumulative meta-analysis of aspirin efficacy after cerebral ischaemia of arterial origin. J Neurol Neurosurg Psychiatry. 1999 Feb;66(2):255.

The Effectiveness of CTA in Diagnosing Acute Pulmonary Embolism

Stein PD, Fowler SE, Goodman LR, et al. Multidetector computed tomography for acute pulmonary embolism. N Engl J Med. 2006 Jun 1;354(22):2317-2327.

Background

The Prospective Investigation of Pulmonary Embolism Diagnosis II (PIOPED II) trial was designed to answer questions about the accuracy of contrast-enhanced multidetector computed tomographic angiography (CTA). Recent studies of the use of single-row or multidetector CTA alone have suggested a low incidence of pulmonary embolism in follow-up of untreated patients with normal findings on CTA.

 

 

The specific goals of this study were to determine the ability of multidetector CTA to rule out or detect pulmonary embolism, and to evaluate whether the addition of computed tomographic venography (CTV) improves the diagnostic accuracy of CTA.

Methods

Using a technique similar to PIOPED I, the investigators performed a prospective, multi-center trial using a composite reference standard to confirm the diagnosis of pulmonary embolism. Once again, for ethical reasons, the use of pulmonary artery digital-subtraction angiography was limited to patients whose diagnosis could neither be confirmed nor ruled out by less invasive tests. In contrast to PIOPED I, a clinical scoring system was used to assess the clinical probability of pulmonary embolism. Central readings were performed on all imaging studies except for venous ultrasonography.

Results

Of the 7,284 patients screened for the study, 3,262 were eligible, and 1,090 were enrolled. Of those, 824 patients received a completed CTA study and a reference standard for analysis. In 51 patients, the quality of the CTA was not suitable for interpretation, and these patients were excluded from the subsequent analysis. Pulmonary embolism was diagnosed in192 patients.

CTA was found to have a sensitivity of 83% and a specificity of 96%, yielding a likelihood ratio for a positive multidetector CTA test of 19.6 (95% confidence interval, 13.3 to 29.0), while the likelihood ratio for a negative test was 0.18 (95% confidence interval, 0.13 to 0.24). The quality of results on CTA-CTV was not adequate for interpretation in 87 patients; when these patients were excluded from analysis, the sensitivity was 90% with a specificity of 95%, yielding likelihood ratios of 16.5 (95% confidence interval, 11.6 to 23.5) for a positive test and 0.11 (95% confidence interval, 0.07 to 0.16) for a negative test.

Conclusions

Multidetector CTA and CTA-CTV perform well when the results of these tests are concordant with pre-test clinical probabilities of pulmonary embolism. CTA-CTV offers slightly increased sensitivity compared with CTA alone, with no significant difference in specificity. If the results of CTA or CTA-CTV are inconsistent with the clinical probability of pulmonary embolism, additional diagnostic testing is indicated.

Commentary

CTA has been used widely, and in many centers has largely replaced other diagnostic tests for pulmonary embolism. This well-done study incorporated recent advances in technology with multidetector CTA-CTV, along with a clinical prediction rule to better estimate pre-test probabilities of pulmonary embolism.2 It is important to recognize that 266 of the 1,090 patients enrolled were not included in the calculations of sensitivity and specificity for CTA-CTV because they did not have interpretable test results.

Although the specificity of both CTA and the CTA-CTV combination were high, the sensitivity was not sufficient to identify all cases of pulmonary embolism. This result contrasts to the recent outcomes studies of CTA, in which low rates of venous thromboembolism were seen in follow-up of patients with negative multidetector CTA.3,4 Although multidetector CTA has a higher sensitivity than single-slice technology, this test may still miss small subsegmental thrombi that might be detected using other diagnostic tests (ventilation-perfusion scintigraphy and/or pulmonary digital-subtraction angiography).

An important take-home message from this study is to recognize once again the importance of utilizing established clinical prediction rules for venous thrombosis and pulmonary embolism (such as the Wells clinical model).2 As with the majority of diagnostic tests at our disposal, when our clinical judgment is in contrast with test results, as in the case of a high likelihood of a potentially fatal disease like pulmonary embolism with a normal CTA result, additional diagnostic testing is necessary.

References

  1. The PIOPED Investigators. Value of the ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.
  2. Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med. 2001 Jul 17;135(2):98-107.
  3. Perrier A, Roy PM, Sanchez O, et al. Multidetector-row computed tomography in suspected pulmonary embolism. N Engl J Med. 2005 Apr;352(17):1760-1768.
  4. van Belle A, Buller HR, Huisman MV, et al. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA. 2006 Jan 11;295(2):172-179.
 

 

Classic Article:

PIOPED Investigators

The PIOPED Investigators. Value of ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.

Background

The risk of untreated pulmonary embolism requires either the diagnosis or the exclusion of this diagnosis when clinical suspicion exists. The reference test for pulmonary embolism, standard pulmonary angiography, is invasive and expensive, and carries with it a measurable procedural risk.

Non-invasive diagnostic tests, including ventilation/perfusion (V/Q) scintigraphy, have been used to detect perfusion defects consistent with pulmonary embolism, though the performance characteristics of this diagnostic test were not well known prior to 1990. This study was designed to evaluate the sensitivity and specificities of ventilation/perfusion lung scans for pulmonary embolism in the acute setting.

Methods

This prospective, multi-center study evaluated V/Q scintigraphy on a random sample of 931 patients. A composite reference standard was used because only 755 patients underwent scintigraphy and pulmonary angiography. Clinical follow-up and subsequent diagnostic testing were employed in untreated patients with low clinical probabilities of pulmonary embolism who did not undergo angiography. Clinical assessment of the probability of pulmonary embolism was determined on the basis of the clinician’s judgment, without systematic prediction rules.

Results

Almost all patients with pulmonary embolism had abnormal ventilation/perfusion lung scans of high, intermediate, or low probability. Unfortunately, most patients without pulmonary embolism also had abnormal studies, limiting the utility of this test. Clinical follow-up and angiography revealed that pulmonary embolism occurred among 12% of patients with low-probability scans.

Conclusions

V/Q scintigraphy is useful in establishing or excluding the diagnosis of pulmonary embolism in only a minority of patients, where clinical suspicion of pulmonary embolism is concordant with the diagnostic test findings. The likelihood of pulmonary embolism in patients with a high pre-test probability of pulmonary embolism and a high probability scan is 95%, while in low probability patients with a low probability or normal scan the probability is 4% or 2%, respectively.

Commentary

This original PIOPED study established the diagnostic characteristics of V/Q scintigraphy and demonstrated, for the first time, evidence of the role of clinical assessment and prior probability in a diagnostic strategy for pulmonary embolism. Although subsequent studies have significantly advanced our knowledge of clinical prediction and diagnostic strategies in venous thromboembolism, the first PIOPED study continues to serve as an example of a high-quality, multi-center diagnostic test study utilizing a composite reference standard in a difficult-to-study disease. Unfortunately, the results of this study demonstrated that V/Q scintigraphy performs well for only a minority of patients. The majority of patients (72%) had clinical probabilities of pulmonary embolism and ventilation/perfusion scan results, which yielded post-test probabilities of 15-86%, leaving, in many cases, enough remaining diagnostic uncertainty to warrant additional testing.—TO TH

Issue
The Hospitalist - 2009(06)
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Factors Influencing the Treatment of COPD

Lindenauer PK, Pekow P, Gao S, et al. Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2006;144:894-903.

Background

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, resulting in more than $18 billion in annual health costs. Acute exacerbations of COPD can lead to respiratory compromise and are one of the 10 leading causes of hospitalization in the United States.

Hospitalists currently have evidence-based guidelines available that recommend therapies for patients with acute exacerbations of COPD. This study was designed to evaluate the practice patterns in the United States and to evaluate the quality of care provided to hospitalized patients based on comparisons with these published guidelines. The authors did not report any conflicts of interest, and this work was performed without external grant support.

Methods

Using administrative data from the 360 hospitals that participate in Perspective, a database developed for measuring healthcare quality and utilization, the authors performed a retrospective cohort study. Patients hospitalized for a primary diagnosis of acute exacerbation of COPD were chosen. Patients with pneumonia were specifically excluded. The outcomes of interest included adherence to the diagnostic and therapeutic recommendations of the joint American College of Physicians and American College of Chest Physicians evidence-based COPD guideline, published in 2001.

Results

Of the 69,820 patients included in the analysis, 33% received “ideal care,” defined as all of the recommended care and none of the non-beneficial interventions. Specific results included varied utilization of recommended care: 95% had chest radiography, 91% received supplemental oxygen, 97% had bronchodilators, 85% were given systemic steroids, and 85% received antibiotics.

Overall, 45% of patients received at least one non-beneficial intervention specified in the guidelines: 24% were treated with methylxanthines, 14% underwent sputum testing, 12% had acute spirometry, 6% received chest physiotherapy, and 2% were given mucolytics.

Older patients and women were more likely to receive ideal care as defined, but hospitals with a higher annual volume of COPD cases were not associated with improved performance in this analysis.

Conclusions

Given a widely accepted evidence-based practice guideline as a benchmark, significant variation exists across hospitals in the quality of care for acute exacerbations of COPD. Opportunities exist to improve the quality of care, in particular by increasing the use of systemic corticosteroids and antibiotic therapy and reducing the utilization of many diagnostic and therapeutic interventions that are not only not recommended but are also potentially harmful.

Commentary

COPD management in the acute inpatient setting is on the horizon as a focus of policymakers, and this study suggests that significant opportunities exist for inpatient physicians to reduce variation in practice and utilize an evidence-based approach to the treatment of acute exacerbations of COPD. This study is limited by its use of administrative data, its inability to use clinical data to best determine appropriate care processes for individual patients, and its retrospective design.

As we move toward external quality metrics for the care of patients with acute exacerbations of COPD, further prospective studies evaluating clinical outcomes of interest, including mortality and readmission rates, are needed to determine the effects of adherence to ideal or recommended care for acute exacerbations of COPD.1-3

References

  1. Snow V, Lascher S, Mottur-Pilson C, et al. Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2001 Apr 3;134(7):595-599.
  2. American Thoracic Society. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, November 1986. Am Rev Respir Dis. 1987 Jul;136(1):225-244.
  3. Agency for Healthcare Research and Quality. Management of Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Rockville, Md.: Agency for Healthcare Research and Quality; 2000.
 

 

The Role of Dipyridamole in the Secondary Prevention of Stroke

ESPRIT Study Group. Aspirin plus dipyridamole versus aspirin alone after cerebral ischaemia of arterial origin (ESPRIT): randomized controlled trial. Lancet. 2006 May 20;367(9623):1665-1673.

Background

To date, studies have resulted in inconsistent results in trials of aspirin versus aspirin in combination with dipyridamole for secondary prevention of ischemic stroke. Four early, smaller studies have yielded non-significant results, in contrast to the statistically significant relative risk reduction seen with the addition of dipyridamole to aspirin in the European Stroke Prevention Study 2 (ESPS-2).1-2

Methods

The European/Australian Stroke Prevention in Reversible Ischaemia Trial (ESPRIT) study group conducted a prospective randomized controlled trial of 2,763 patients with transient ischemic attacks or minor ischemic stroke of presumed arterial origin who received aspirin (30-325 mg daily) with or without dipyridamole (200 mg twice daily) as secondary prevention. The primary outcome for this study was a composite of death from vascular causes, nonfatal stroke, nonfatal myocardial infarction, or major bleeding complication. Mean follow-up of patients enrolled was 3.5 years.

Results

In an intention-to-treat analysis, the primary combined endpoint occurred in 16% (216) of the patients on aspirin alone (median aspirin dose was 75 mg in both groups) compared with 13% (173) of the patients on aspirin plus dipyridamole. This result was statistically significant, with an absolute risk reduction of 1% per year. As noted in other trials, patients on dipyridamole discontinued their study medication more frequently than patients on aspirin alone, mostly due to headache.

Conclusions

The results of this trial, taken in the context of previously published data, support the combination of aspirin plus dipyridamole over aspirin alone for the secondary prevention of ischemic stroke of presumed arterial origin. Addition of these data to the previous meta-analysis of trials resulted in a statistically significant risk ratio for the composite endpoint of 0.82 (95% confidence interval, 0.74-0.91).1

Commentary

Ischemic stroke and transient ischemic attacks remain a challenge to effectively manage medically and are appropriately greatly feared health complications for many patients, resulting in significant morbidity and mortality. Prior studies of secondary prevention with aspirin therapy have demonstrated only a modest reduction in vascular complications in these patients.3-4

The results of this trial are consistent with data from the Second European Stroke Prevention Study, and in combination, these data confirm that the addition of dipyridamole for patients who can tolerate it offers significant benefit.2 The magnitude of the effect results in a number needed to treat of 100 patients for one year to prevent one vascular death, stroke, or myocardial infarction. Given the clinical significance of these outcomes, many patients may prefer a trial on combination therapy.

References

  1. Antithrombotic Trialists’ Collaboration. Collaborative meta-analysis of randomised trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients. BMJ. 2002 Jan 12;324(7329):71-86.
  2. Diener HC, Cunha L, Forbes C, et al. European Stroke Prevention Study. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke. J Neurol Sci. 1996;143:1-13.
  3. Warlow C. Secondary prevention of stroke. Lancet. 1992;339:724-727.
  4. Algra A, van Gijn J. Cumulative meta-analysis of aspirin efficacy after cerebral ischaemia of arterial origin. J Neurol Neurosurg Psychiatry. 1999 Feb;66(2):255.

The Effectiveness of CTA in Diagnosing Acute Pulmonary Embolism

Stein PD, Fowler SE, Goodman LR, et al. Multidetector computed tomography for acute pulmonary embolism. N Engl J Med. 2006 Jun 1;354(22):2317-2327.

Background

The Prospective Investigation of Pulmonary Embolism Diagnosis II (PIOPED II) trial was designed to answer questions about the accuracy of contrast-enhanced multidetector computed tomographic angiography (CTA). Recent studies of the use of single-row or multidetector CTA alone have suggested a low incidence of pulmonary embolism in follow-up of untreated patients with normal findings on CTA.

 

 

The specific goals of this study were to determine the ability of multidetector CTA to rule out or detect pulmonary embolism, and to evaluate whether the addition of computed tomographic venography (CTV) improves the diagnostic accuracy of CTA.

Methods

Using a technique similar to PIOPED I, the investigators performed a prospective, multi-center trial using a composite reference standard to confirm the diagnosis of pulmonary embolism. Once again, for ethical reasons, the use of pulmonary artery digital-subtraction angiography was limited to patients whose diagnosis could neither be confirmed nor ruled out by less invasive tests. In contrast to PIOPED I, a clinical scoring system was used to assess the clinical probability of pulmonary embolism. Central readings were performed on all imaging studies except for venous ultrasonography.

Results

Of the 7,284 patients screened for the study, 3,262 were eligible, and 1,090 were enrolled. Of those, 824 patients received a completed CTA study and a reference standard for analysis. In 51 patients, the quality of the CTA was not suitable for interpretation, and these patients were excluded from the subsequent analysis. Pulmonary embolism was diagnosed in192 patients.

CTA was found to have a sensitivity of 83% and a specificity of 96%, yielding a likelihood ratio for a positive multidetector CTA test of 19.6 (95% confidence interval, 13.3 to 29.0), while the likelihood ratio for a negative test was 0.18 (95% confidence interval, 0.13 to 0.24). The quality of results on CTA-CTV was not adequate for interpretation in 87 patients; when these patients were excluded from analysis, the sensitivity was 90% with a specificity of 95%, yielding likelihood ratios of 16.5 (95% confidence interval, 11.6 to 23.5) for a positive test and 0.11 (95% confidence interval, 0.07 to 0.16) for a negative test.

Conclusions

Multidetector CTA and CTA-CTV perform well when the results of these tests are concordant with pre-test clinical probabilities of pulmonary embolism. CTA-CTV offers slightly increased sensitivity compared with CTA alone, with no significant difference in specificity. If the results of CTA or CTA-CTV are inconsistent with the clinical probability of pulmonary embolism, additional diagnostic testing is indicated.

Commentary

CTA has been used widely, and in many centers has largely replaced other diagnostic tests for pulmonary embolism. This well-done study incorporated recent advances in technology with multidetector CTA-CTV, along with a clinical prediction rule to better estimate pre-test probabilities of pulmonary embolism.2 It is important to recognize that 266 of the 1,090 patients enrolled were not included in the calculations of sensitivity and specificity for CTA-CTV because they did not have interpretable test results.

Although the specificity of both CTA and the CTA-CTV combination were high, the sensitivity was not sufficient to identify all cases of pulmonary embolism. This result contrasts to the recent outcomes studies of CTA, in which low rates of venous thromboembolism were seen in follow-up of patients with negative multidetector CTA.3,4 Although multidetector CTA has a higher sensitivity than single-slice technology, this test may still miss small subsegmental thrombi that might be detected using other diagnostic tests (ventilation-perfusion scintigraphy and/or pulmonary digital-subtraction angiography).

An important take-home message from this study is to recognize once again the importance of utilizing established clinical prediction rules for venous thrombosis and pulmonary embolism (such as the Wells clinical model).2 As with the majority of diagnostic tests at our disposal, when our clinical judgment is in contrast with test results, as in the case of a high likelihood of a potentially fatal disease like pulmonary embolism with a normal CTA result, additional diagnostic testing is necessary.

References

  1. The PIOPED Investigators. Value of the ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.
  2. Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med. 2001 Jul 17;135(2):98-107.
  3. Perrier A, Roy PM, Sanchez O, et al. Multidetector-row computed tomography in suspected pulmonary embolism. N Engl J Med. 2005 Apr;352(17):1760-1768.
  4. van Belle A, Buller HR, Huisman MV, et al. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA. 2006 Jan 11;295(2):172-179.
 

 

Classic Article:

PIOPED Investigators

The PIOPED Investigators. Value of ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.

Background

The risk of untreated pulmonary embolism requires either the diagnosis or the exclusion of this diagnosis when clinical suspicion exists. The reference test for pulmonary embolism, standard pulmonary angiography, is invasive and expensive, and carries with it a measurable procedural risk.

Non-invasive diagnostic tests, including ventilation/perfusion (V/Q) scintigraphy, have been used to detect perfusion defects consistent with pulmonary embolism, though the performance characteristics of this diagnostic test were not well known prior to 1990. This study was designed to evaluate the sensitivity and specificities of ventilation/perfusion lung scans for pulmonary embolism in the acute setting.

Methods

This prospective, multi-center study evaluated V/Q scintigraphy on a random sample of 931 patients. A composite reference standard was used because only 755 patients underwent scintigraphy and pulmonary angiography. Clinical follow-up and subsequent diagnostic testing were employed in untreated patients with low clinical probabilities of pulmonary embolism who did not undergo angiography. Clinical assessment of the probability of pulmonary embolism was determined on the basis of the clinician’s judgment, without systematic prediction rules.

Results

Almost all patients with pulmonary embolism had abnormal ventilation/perfusion lung scans of high, intermediate, or low probability. Unfortunately, most patients without pulmonary embolism also had abnormal studies, limiting the utility of this test. Clinical follow-up and angiography revealed that pulmonary embolism occurred among 12% of patients with low-probability scans.

Conclusions

V/Q scintigraphy is useful in establishing or excluding the diagnosis of pulmonary embolism in only a minority of patients, where clinical suspicion of pulmonary embolism is concordant with the diagnostic test findings. The likelihood of pulmonary embolism in patients with a high pre-test probability of pulmonary embolism and a high probability scan is 95%, while in low probability patients with a low probability or normal scan the probability is 4% or 2%, respectively.

Commentary

This original PIOPED study established the diagnostic characteristics of V/Q scintigraphy and demonstrated, for the first time, evidence of the role of clinical assessment and prior probability in a diagnostic strategy for pulmonary embolism. Although subsequent studies have significantly advanced our knowledge of clinical prediction and diagnostic strategies in venous thromboembolism, the first PIOPED study continues to serve as an example of a high-quality, multi-center diagnostic test study utilizing a composite reference standard in a difficult-to-study disease. Unfortunately, the results of this study demonstrated that V/Q scintigraphy performs well for only a minority of patients. The majority of patients (72%) had clinical probabilities of pulmonary embolism and ventilation/perfusion scan results, which yielded post-test probabilities of 15-86%, leaving, in many cases, enough remaining diagnostic uncertainty to warrant additional testing.—TO TH

Factors Influencing the Treatment of COPD

Lindenauer PK, Pekow P, Gao S, et al. Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2006;144:894-903.

Background

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, resulting in more than $18 billion in annual health costs. Acute exacerbations of COPD can lead to respiratory compromise and are one of the 10 leading causes of hospitalization in the United States.

Hospitalists currently have evidence-based guidelines available that recommend therapies for patients with acute exacerbations of COPD. This study was designed to evaluate the practice patterns in the United States and to evaluate the quality of care provided to hospitalized patients based on comparisons with these published guidelines. The authors did not report any conflicts of interest, and this work was performed without external grant support.

Methods

Using administrative data from the 360 hospitals that participate in Perspective, a database developed for measuring healthcare quality and utilization, the authors performed a retrospective cohort study. Patients hospitalized for a primary diagnosis of acute exacerbation of COPD were chosen. Patients with pneumonia were specifically excluded. The outcomes of interest included adherence to the diagnostic and therapeutic recommendations of the joint American College of Physicians and American College of Chest Physicians evidence-based COPD guideline, published in 2001.

Results

Of the 69,820 patients included in the analysis, 33% received “ideal care,” defined as all of the recommended care and none of the non-beneficial interventions. Specific results included varied utilization of recommended care: 95% had chest radiography, 91% received supplemental oxygen, 97% had bronchodilators, 85% were given systemic steroids, and 85% received antibiotics.

Overall, 45% of patients received at least one non-beneficial intervention specified in the guidelines: 24% were treated with methylxanthines, 14% underwent sputum testing, 12% had acute spirometry, 6% received chest physiotherapy, and 2% were given mucolytics.

Older patients and women were more likely to receive ideal care as defined, but hospitals with a higher annual volume of COPD cases were not associated with improved performance in this analysis.

Conclusions

Given a widely accepted evidence-based practice guideline as a benchmark, significant variation exists across hospitals in the quality of care for acute exacerbations of COPD. Opportunities exist to improve the quality of care, in particular by increasing the use of systemic corticosteroids and antibiotic therapy and reducing the utilization of many diagnostic and therapeutic interventions that are not only not recommended but are also potentially harmful.

Commentary

COPD management in the acute inpatient setting is on the horizon as a focus of policymakers, and this study suggests that significant opportunities exist for inpatient physicians to reduce variation in practice and utilize an evidence-based approach to the treatment of acute exacerbations of COPD. This study is limited by its use of administrative data, its inability to use clinical data to best determine appropriate care processes for individual patients, and its retrospective design.

As we move toward external quality metrics for the care of patients with acute exacerbations of COPD, further prospective studies evaluating clinical outcomes of interest, including mortality and readmission rates, are needed to determine the effects of adherence to ideal or recommended care for acute exacerbations of COPD.1-3

References

  1. Snow V, Lascher S, Mottur-Pilson C, et al. Evidence base for management of acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2001 Apr 3;134(7):595-599.
  2. American Thoracic Society. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, November 1986. Am Rev Respir Dis. 1987 Jul;136(1):225-244.
  3. Agency for Healthcare Research and Quality. Management of Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Rockville, Md.: Agency for Healthcare Research and Quality; 2000.
 

 

The Role of Dipyridamole in the Secondary Prevention of Stroke

ESPRIT Study Group. Aspirin plus dipyridamole versus aspirin alone after cerebral ischaemia of arterial origin (ESPRIT): randomized controlled trial. Lancet. 2006 May 20;367(9623):1665-1673.

Background

To date, studies have resulted in inconsistent results in trials of aspirin versus aspirin in combination with dipyridamole for secondary prevention of ischemic stroke. Four early, smaller studies have yielded non-significant results, in contrast to the statistically significant relative risk reduction seen with the addition of dipyridamole to aspirin in the European Stroke Prevention Study 2 (ESPS-2).1-2

Methods

The European/Australian Stroke Prevention in Reversible Ischaemia Trial (ESPRIT) study group conducted a prospective randomized controlled trial of 2,763 patients with transient ischemic attacks or minor ischemic stroke of presumed arterial origin who received aspirin (30-325 mg daily) with or without dipyridamole (200 mg twice daily) as secondary prevention. The primary outcome for this study was a composite of death from vascular causes, nonfatal stroke, nonfatal myocardial infarction, or major bleeding complication. Mean follow-up of patients enrolled was 3.5 years.

Results

In an intention-to-treat analysis, the primary combined endpoint occurred in 16% (216) of the patients on aspirin alone (median aspirin dose was 75 mg in both groups) compared with 13% (173) of the patients on aspirin plus dipyridamole. This result was statistically significant, with an absolute risk reduction of 1% per year. As noted in other trials, patients on dipyridamole discontinued their study medication more frequently than patients on aspirin alone, mostly due to headache.

Conclusions

The results of this trial, taken in the context of previously published data, support the combination of aspirin plus dipyridamole over aspirin alone for the secondary prevention of ischemic stroke of presumed arterial origin. Addition of these data to the previous meta-analysis of trials resulted in a statistically significant risk ratio for the composite endpoint of 0.82 (95% confidence interval, 0.74-0.91).1

Commentary

Ischemic stroke and transient ischemic attacks remain a challenge to effectively manage medically and are appropriately greatly feared health complications for many patients, resulting in significant morbidity and mortality. Prior studies of secondary prevention with aspirin therapy have demonstrated only a modest reduction in vascular complications in these patients.3-4

The results of this trial are consistent with data from the Second European Stroke Prevention Study, and in combination, these data confirm that the addition of dipyridamole for patients who can tolerate it offers significant benefit.2 The magnitude of the effect results in a number needed to treat of 100 patients for one year to prevent one vascular death, stroke, or myocardial infarction. Given the clinical significance of these outcomes, many patients may prefer a trial on combination therapy.

References

  1. Antithrombotic Trialists’ Collaboration. Collaborative meta-analysis of randomised trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients. BMJ. 2002 Jan 12;324(7329):71-86.
  2. Diener HC, Cunha L, Forbes C, et al. European Stroke Prevention Study. Dipyridamole and acetylsalicylic acid in the secondary prevention of stroke. J Neurol Sci. 1996;143:1-13.
  3. Warlow C. Secondary prevention of stroke. Lancet. 1992;339:724-727.
  4. Algra A, van Gijn J. Cumulative meta-analysis of aspirin efficacy after cerebral ischaemia of arterial origin. J Neurol Neurosurg Psychiatry. 1999 Feb;66(2):255.

The Effectiveness of CTA in Diagnosing Acute Pulmonary Embolism

Stein PD, Fowler SE, Goodman LR, et al. Multidetector computed tomography for acute pulmonary embolism. N Engl J Med. 2006 Jun 1;354(22):2317-2327.

Background

The Prospective Investigation of Pulmonary Embolism Diagnosis II (PIOPED II) trial was designed to answer questions about the accuracy of contrast-enhanced multidetector computed tomographic angiography (CTA). Recent studies of the use of single-row or multidetector CTA alone have suggested a low incidence of pulmonary embolism in follow-up of untreated patients with normal findings on CTA.

 

 

The specific goals of this study were to determine the ability of multidetector CTA to rule out or detect pulmonary embolism, and to evaluate whether the addition of computed tomographic venography (CTV) improves the diagnostic accuracy of CTA.

Methods

Using a technique similar to PIOPED I, the investigators performed a prospective, multi-center trial using a composite reference standard to confirm the diagnosis of pulmonary embolism. Once again, for ethical reasons, the use of pulmonary artery digital-subtraction angiography was limited to patients whose diagnosis could neither be confirmed nor ruled out by less invasive tests. In contrast to PIOPED I, a clinical scoring system was used to assess the clinical probability of pulmonary embolism. Central readings were performed on all imaging studies except for venous ultrasonography.

Results

Of the 7,284 patients screened for the study, 3,262 were eligible, and 1,090 were enrolled. Of those, 824 patients received a completed CTA study and a reference standard for analysis. In 51 patients, the quality of the CTA was not suitable for interpretation, and these patients were excluded from the subsequent analysis. Pulmonary embolism was diagnosed in192 patients.

CTA was found to have a sensitivity of 83% and a specificity of 96%, yielding a likelihood ratio for a positive multidetector CTA test of 19.6 (95% confidence interval, 13.3 to 29.0), while the likelihood ratio for a negative test was 0.18 (95% confidence interval, 0.13 to 0.24). The quality of results on CTA-CTV was not adequate for interpretation in 87 patients; when these patients were excluded from analysis, the sensitivity was 90% with a specificity of 95%, yielding likelihood ratios of 16.5 (95% confidence interval, 11.6 to 23.5) for a positive test and 0.11 (95% confidence interval, 0.07 to 0.16) for a negative test.

Conclusions

Multidetector CTA and CTA-CTV perform well when the results of these tests are concordant with pre-test clinical probabilities of pulmonary embolism. CTA-CTV offers slightly increased sensitivity compared with CTA alone, with no significant difference in specificity. If the results of CTA or CTA-CTV are inconsistent with the clinical probability of pulmonary embolism, additional diagnostic testing is indicated.

Commentary

CTA has been used widely, and in many centers has largely replaced other diagnostic tests for pulmonary embolism. This well-done study incorporated recent advances in technology with multidetector CTA-CTV, along with a clinical prediction rule to better estimate pre-test probabilities of pulmonary embolism.2 It is important to recognize that 266 of the 1,090 patients enrolled were not included in the calculations of sensitivity and specificity for CTA-CTV because they did not have interpretable test results.

Although the specificity of both CTA and the CTA-CTV combination were high, the sensitivity was not sufficient to identify all cases of pulmonary embolism. This result contrasts to the recent outcomes studies of CTA, in which low rates of venous thromboembolism were seen in follow-up of patients with negative multidetector CTA.3,4 Although multidetector CTA has a higher sensitivity than single-slice technology, this test may still miss small subsegmental thrombi that might be detected using other diagnostic tests (ventilation-perfusion scintigraphy and/or pulmonary digital-subtraction angiography).

An important take-home message from this study is to recognize once again the importance of utilizing established clinical prediction rules for venous thrombosis and pulmonary embolism (such as the Wells clinical model).2 As with the majority of diagnostic tests at our disposal, when our clinical judgment is in contrast with test results, as in the case of a high likelihood of a potentially fatal disease like pulmonary embolism with a normal CTA result, additional diagnostic testing is necessary.

References

  1. The PIOPED Investigators. Value of the ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.
  2. Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med. 2001 Jul 17;135(2):98-107.
  3. Perrier A, Roy PM, Sanchez O, et al. Multidetector-row computed tomography in suspected pulmonary embolism. N Engl J Med. 2005 Apr;352(17):1760-1768.
  4. van Belle A, Buller HR, Huisman MV, et al. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA. 2006 Jan 11;295(2):172-179.
 

 

Classic Article:

PIOPED Investigators

The PIOPED Investigators. Value of ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED). JAMA. 1990;263:2753-2759.

Background

The risk of untreated pulmonary embolism requires either the diagnosis or the exclusion of this diagnosis when clinical suspicion exists. The reference test for pulmonary embolism, standard pulmonary angiography, is invasive and expensive, and carries with it a measurable procedural risk.

Non-invasive diagnostic tests, including ventilation/perfusion (V/Q) scintigraphy, have been used to detect perfusion defects consistent with pulmonary embolism, though the performance characteristics of this diagnostic test were not well known prior to 1990. This study was designed to evaluate the sensitivity and specificities of ventilation/perfusion lung scans for pulmonary embolism in the acute setting.

Methods

This prospective, multi-center study evaluated V/Q scintigraphy on a random sample of 931 patients. A composite reference standard was used because only 755 patients underwent scintigraphy and pulmonary angiography. Clinical follow-up and subsequent diagnostic testing were employed in untreated patients with low clinical probabilities of pulmonary embolism who did not undergo angiography. Clinical assessment of the probability of pulmonary embolism was determined on the basis of the clinician’s judgment, without systematic prediction rules.

Results

Almost all patients with pulmonary embolism had abnormal ventilation/perfusion lung scans of high, intermediate, or low probability. Unfortunately, most patients without pulmonary embolism also had abnormal studies, limiting the utility of this test. Clinical follow-up and angiography revealed that pulmonary embolism occurred among 12% of patients with low-probability scans.

Conclusions

V/Q scintigraphy is useful in establishing or excluding the diagnosis of pulmonary embolism in only a minority of patients, where clinical suspicion of pulmonary embolism is concordant with the diagnostic test findings. The likelihood of pulmonary embolism in patients with a high pre-test probability of pulmonary embolism and a high probability scan is 95%, while in low probability patients with a low probability or normal scan the probability is 4% or 2%, respectively.

Commentary

This original PIOPED study established the diagnostic characteristics of V/Q scintigraphy and demonstrated, for the first time, evidence of the role of clinical assessment and prior probability in a diagnostic strategy for pulmonary embolism. Although subsequent studies have significantly advanced our knowledge of clinical prediction and diagnostic strategies in venous thromboembolism, the first PIOPED study continues to serve as an example of a high-quality, multi-center diagnostic test study utilizing a composite reference standard in a difficult-to-study disease. Unfortunately, the results of this study demonstrated that V/Q scintigraphy performs well for only a minority of patients. The majority of patients (72%) had clinical probabilities of pulmonary embolism and ventilation/perfusion scan results, which yielded post-test probabilities of 15-86%, leaving, in many cases, enough remaining diagnostic uncertainty to warrant additional testing.—TO TH

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Andrew Fishmann, MD, FCCP, FACP, listened for more than an hour as an award-winning hospitalist talked about strategies to improve communication between physicians and administrators. Brian Bossard, MD, FACP, FHM, gleaned lessons from case studies of how two HM groups (HMGs) navigated the adolescent years. And dozens more hospitalists sat still for the bulk of an hour as they were given a nuts-and-bolts tutorial of HM finances.

Welcome to SHM’s new practice management track, a three-day series of training sessions that debuted at HM09 and focuses on the inner workings of HMGs—from startup to coding and billing to expansion. “There’s always a quality track, there’s always a clinical track, and there’s always a leadership track,” says Kimberly Dickinson, vice president of operations for Cogent Healthcare in Brentwood, Tenn., and an SHM Career Satisfaction Task Force member. “This was trying to bring together some of the nonclinical aspects.”

The experiment drew positive reviews, if rooms crowded with physicians—some lined up along the walls—are any indication. Dr. Fishmann, a Cogent co-founder who is practicing as a hospitalist with California Lung Associates in Los Angeles, says the courses give younger hospitalists different perspectives. “It’s taking doctors a little out of the hospital and putting them in the boardroom,” he says. “You don’t have a choice. … You need to be involved and have more input.”

The practice management courses are structured to give a broad overview of nearly every facet of opening and operating an HMG. One popular course, which ran nearly 15 minutes long because of participant questions, focused on managing HMG growth. Another session spent more than an hour taking physicians through comanagement issues that arise during collaborations with surgeons.

Real-Life Experiences

Most of the courses were led by familiar SHM leaders. But several attendees said they enjoyed sessions that were led by rank-and-file hospitalists and administrators who live the front-line struggles every day.

The “Case Studies in Managing Program Growth” course featured detailed explanations of the growing pains of HM programs in Michigan and Massachusetts. In the former case, Carole Montgomery, MD, talked about how the Michigan Medical PC group she helped start in western Michigan struggled to formalize certain procedures when it signed its first contract with a hospital in 2002. The group doubled its hospitalist roster and instantly went from an informal HM practice in which everyone knew each other and had relatively similar opinions to a business in which it was unclear who would make major decisions.

In response, Dr. Montgomery’s group crafted a mission statement, created a hospitalist executive committee to make routine operational decisions, and changed how it negotiated contracts with hospitals. Soon after, the group fired its first physician. Last year, the group instituted “internal governance guidelines” to make management decisions clearer. Dr. Montgomery says each of the developments taught her that solving major issues takes patience and a willingness to continually adapt. “I thought I was done each time,” she says. “Now I realize it’s an interactive process.”

Dr. Bossard

Results-Oriented

Peter Short, MD, FAAP, CPE, medical director of Northeast Hospital Corp., shared his recent struggles to hire one hospitalist and expand night coverage. Hospitalists in Dr. Short’s service, who practice at Beverly Hospital in Beverly, Mass., resisted the change at first, not wanting to add additional night-shift responsibility. Dr. Short also spoke about financial concerns to the group and the hospital. After explaining the pros and cons of hiring a sixth rounder, the hospitalists embraced the idea. So far, hospital administrators have had zero complaints, as the first three months of data show a reduction in length of stay by 0.3 days and an average cost decrease of roughly $500 per case.

 

 

Dr. Short also explained an unexpected byproduct of the hiring: Night work proved so popular that the hospitalists have demanded that they receive a specified number of overnight shifts a year. “They went from not wanting it to fighting for it,” Dr. Short says proudly. “There is no one-size-fits-all for hospital programs.”

Dr. Bossard, who founded Inpatient Physician Associates in Lincoln, Neb., says the types of detail-oriented presentations made by Drs. Montgomery and Short are useful if physicians learn lessons they can take home and adapt to their practice. “If it’s a lot of smoke without an action item or a bullet point we can latch on to,” he says, “it’s not as helpful.”

Richard Quinn is a freelance writer based in New Jersey.

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Andrew Fishmann, MD, FCCP, FACP, listened for more than an hour as an award-winning hospitalist talked about strategies to improve communication between physicians and administrators. Brian Bossard, MD, FACP, FHM, gleaned lessons from case studies of how two HM groups (HMGs) navigated the adolescent years. And dozens more hospitalists sat still for the bulk of an hour as they were given a nuts-and-bolts tutorial of HM finances.

Welcome to SHM’s new practice management track, a three-day series of training sessions that debuted at HM09 and focuses on the inner workings of HMGs—from startup to coding and billing to expansion. “There’s always a quality track, there’s always a clinical track, and there’s always a leadership track,” says Kimberly Dickinson, vice president of operations for Cogent Healthcare in Brentwood, Tenn., and an SHM Career Satisfaction Task Force member. “This was trying to bring together some of the nonclinical aspects.”

The experiment drew positive reviews, if rooms crowded with physicians—some lined up along the walls—are any indication. Dr. Fishmann, a Cogent co-founder who is practicing as a hospitalist with California Lung Associates in Los Angeles, says the courses give younger hospitalists different perspectives. “It’s taking doctors a little out of the hospital and putting them in the boardroom,” he says. “You don’t have a choice. … You need to be involved and have more input.”

The practice management courses are structured to give a broad overview of nearly every facet of opening and operating an HMG. One popular course, which ran nearly 15 minutes long because of participant questions, focused on managing HMG growth. Another session spent more than an hour taking physicians through comanagement issues that arise during collaborations with surgeons.

Real-Life Experiences

Most of the courses were led by familiar SHM leaders. But several attendees said they enjoyed sessions that were led by rank-and-file hospitalists and administrators who live the front-line struggles every day.

The “Case Studies in Managing Program Growth” course featured detailed explanations of the growing pains of HM programs in Michigan and Massachusetts. In the former case, Carole Montgomery, MD, talked about how the Michigan Medical PC group she helped start in western Michigan struggled to formalize certain procedures when it signed its first contract with a hospital in 2002. The group doubled its hospitalist roster and instantly went from an informal HM practice in which everyone knew each other and had relatively similar opinions to a business in which it was unclear who would make major decisions.

In response, Dr. Montgomery’s group crafted a mission statement, created a hospitalist executive committee to make routine operational decisions, and changed how it negotiated contracts with hospitals. Soon after, the group fired its first physician. Last year, the group instituted “internal governance guidelines” to make management decisions clearer. Dr. Montgomery says each of the developments taught her that solving major issues takes patience and a willingness to continually adapt. “I thought I was done each time,” she says. “Now I realize it’s an interactive process.”

Dr. Bossard

Results-Oriented

Peter Short, MD, FAAP, CPE, medical director of Northeast Hospital Corp., shared his recent struggles to hire one hospitalist and expand night coverage. Hospitalists in Dr. Short’s service, who practice at Beverly Hospital in Beverly, Mass., resisted the change at first, not wanting to add additional night-shift responsibility. Dr. Short also spoke about financial concerns to the group and the hospital. After explaining the pros and cons of hiring a sixth rounder, the hospitalists embraced the idea. So far, hospital administrators have had zero complaints, as the first three months of data show a reduction in length of stay by 0.3 days and an average cost decrease of roughly $500 per case.

 

 

Dr. Short also explained an unexpected byproduct of the hiring: Night work proved so popular that the hospitalists have demanded that they receive a specified number of overnight shifts a year. “They went from not wanting it to fighting for it,” Dr. Short says proudly. “There is no one-size-fits-all for hospital programs.”

Dr. Bossard, who founded Inpatient Physician Associates in Lincoln, Neb., says the types of detail-oriented presentations made by Drs. Montgomery and Short are useful if physicians learn lessons they can take home and adapt to their practice. “If it’s a lot of smoke without an action item or a bullet point we can latch on to,” he says, “it’s not as helpful.”

Richard Quinn is a freelance writer based in New Jersey.

Andrew Fishmann, MD, FCCP, FACP, listened for more than an hour as an award-winning hospitalist talked about strategies to improve communication between physicians and administrators. Brian Bossard, MD, FACP, FHM, gleaned lessons from case studies of how two HM groups (HMGs) navigated the adolescent years. And dozens more hospitalists sat still for the bulk of an hour as they were given a nuts-and-bolts tutorial of HM finances.

Welcome to SHM’s new practice management track, a three-day series of training sessions that debuted at HM09 and focuses on the inner workings of HMGs—from startup to coding and billing to expansion. “There’s always a quality track, there’s always a clinical track, and there’s always a leadership track,” says Kimberly Dickinson, vice president of operations for Cogent Healthcare in Brentwood, Tenn., and an SHM Career Satisfaction Task Force member. “This was trying to bring together some of the nonclinical aspects.”

The experiment drew positive reviews, if rooms crowded with physicians—some lined up along the walls—are any indication. Dr. Fishmann, a Cogent co-founder who is practicing as a hospitalist with California Lung Associates in Los Angeles, says the courses give younger hospitalists different perspectives. “It’s taking doctors a little out of the hospital and putting them in the boardroom,” he says. “You don’t have a choice. … You need to be involved and have more input.”

The practice management courses are structured to give a broad overview of nearly every facet of opening and operating an HMG. One popular course, which ran nearly 15 minutes long because of participant questions, focused on managing HMG growth. Another session spent more than an hour taking physicians through comanagement issues that arise during collaborations with surgeons.

Real-Life Experiences

Most of the courses were led by familiar SHM leaders. But several attendees said they enjoyed sessions that were led by rank-and-file hospitalists and administrators who live the front-line struggles every day.

The “Case Studies in Managing Program Growth” course featured detailed explanations of the growing pains of HM programs in Michigan and Massachusetts. In the former case, Carole Montgomery, MD, talked about how the Michigan Medical PC group she helped start in western Michigan struggled to formalize certain procedures when it signed its first contract with a hospital in 2002. The group doubled its hospitalist roster and instantly went from an informal HM practice in which everyone knew each other and had relatively similar opinions to a business in which it was unclear who would make major decisions.

In response, Dr. Montgomery’s group crafted a mission statement, created a hospitalist executive committee to make routine operational decisions, and changed how it negotiated contracts with hospitals. Soon after, the group fired its first physician. Last year, the group instituted “internal governance guidelines” to make management decisions clearer. Dr. Montgomery says each of the developments taught her that solving major issues takes patience and a willingness to continually adapt. “I thought I was done each time,” she says. “Now I realize it’s an interactive process.”

Dr. Bossard

Results-Oriented

Peter Short, MD, FAAP, CPE, medical director of Northeast Hospital Corp., shared his recent struggles to hire one hospitalist and expand night coverage. Hospitalists in Dr. Short’s service, who practice at Beverly Hospital in Beverly, Mass., resisted the change at first, not wanting to add additional night-shift responsibility. Dr. Short also spoke about financial concerns to the group and the hospital. After explaining the pros and cons of hiring a sixth rounder, the hospitalists embraced the idea. So far, hospital administrators have had zero complaints, as the first three months of data show a reduction in length of stay by 0.3 days and an average cost decrease of roughly $500 per case.

 

 

Dr. Short also explained an unexpected byproduct of the hiring: Night work proved so popular that the hospitalists have demanded that they receive a specified number of overnight shifts a year. “They went from not wanting it to fighting for it,” Dr. Short says proudly. “There is no one-size-fits-all for hospital programs.”

Dr. Bossard, who founded Inpatient Physician Associates in Lincoln, Neb., says the types of detail-oriented presentations made by Drs. Montgomery and Short are useful if physicians learn lessons they can take home and adapt to their practice. “If it’s a lot of smoke without an action item or a bullet point we can latch on to,” he says, “it’s not as helpful.”

Richard Quinn is a freelance writer based in New Jersey.

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Reporting Hospital Quality

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Public reporting of hospital quality: Recommendations to benefit patients and hospitals

Acknowledging striking deficiencies in the quality and safety of healthcare, the Institute of Medicine, policy makers, and payors have called for transformation of the US healthcare system.1 Public reporting of hospital performance is one key strategy for accelerating improvement2 and may improve quality in several ways. First, feedback about performance relative to peers may stimulate quality improvement activities by appealing to professionalism. Second, the desire to preserve one's reputation by not appearing on a list of poor performers may be a powerful incentive. Finally, patients and referring providers could use reports to select high‐quality hospitals, thereby shifting care from low‐quality to high‐quality hospitals and stimulating quality improvement efforts to maintain or enhance market share.

Almost 20 years after New York and Pennsylvania began reporting cardiac surgery outcomes,3 the evidence that public reporting improves healthcare quality is equivocal.4 Moreover, stakeholders have embraced public reporting to differing degrees. Public reporting does lead to greater engagement in quality improvement activities,58 and additional financial incentives provide modest incremental benefits.9 Purchasers, too, are starting to pay attention.10 In New York State, payors appear to contract more with high‐quality surgeons and avoid poorly performing outliers.11 Some payors are creating tiered systems, assigning higher patient copayments for hospitals with poor quality metrics. These new systems have not been rigorously studied and should raise concern among hospitals.12

In contrast to hospitals and payors, patients have been slow to embrace public reporting. In a survey of coronary artery bypass graft (CABG) patients in Pennsylvania, only 2% said that public reporting of mortality rates affected their decision making.13 Eight years later, only 11% of patients sought information about hospitals before deciding on elective major surgery,14 although a majority of patients in both studies expressed interest in the information. It is not clear whether recent proliferation of information on the internet will change patient behavior, but to date public reporting appears not to effect market share.5, 15, 16

Barriers to patients' use of public reporting include difficulty accessing the information, lack of trust, information that is not salient, and data that are difficult to interpret.17 In the absence of consensus on what or how to report, a growing number of organizations, including state and federal government, accrediting bodies, private foundations, and for‐profit companies report a variety of measures relating to structure, processes, and outcomes. Although these sites purport to target consumers, they sometimes offer conflicting information18 and are not easily interpreted by lay readers.19

To realize the benefits of public reporting, and minimize the unintended consequences, rating systems must report salient information in a way that is comprehensible to patients and trusted by the doctors who advise them. At the same time, they should be fair to hospitals and offer useful data for quality improvement. We offer 10 recommendations for improving the public reporting of healthcare quality information: 5 describing what to report and 5 detailing how it should be reported (Figure 1). We also examine 3 leading performance reporting programs to see how well they implement these recommendations.

Figure 1
Ten recommendations for public reporting of hospital quality.

Recommendations to Make Data Salient for Patients

1. Prioritize Elective Procedures

Hospital quality is not uniform across conditions.2 For data to be salient, then, it should be disease‐specific and focus on common elective procedures, for which consumer choice is possible. Table 1 compares 3 popular reporting services. Hospital Compare, produced by the Center for Medicare Services (CMS, US Department of Health and Human Services, Washington, DC), provides process of care measures for 4 conditions, 3 of which are not elective. The fourth, surgical infection prevention, contains 5 measures3 related to perioperative antibiotics and 2 related to thromboembolism prophylaxisfor all surgical cases. Recently, more conditions have been added, but reports are limited to the number of cases and mean Medicare charge. By year 2011, however, Hospital Compare will offer many new measures, including rates of central line infection, ventilator‐associated pneumonia, and surgical site infection. HealthGrades, a private company, offers comparative mortality rates on over 30 diagnoses, of which 15 can be considered elective, at least some of the time. Only the Leapfrog group, an industry consortium, focuses exclusively on elective procedures, offering volume measures on 7 and outcome measures on 2.

Three Popular Quality Reporting Services' Adherence to the 10 Recommendations
RuleHospital CompareHealthGradesLeapfrog
  • Abbreviations: AMI, acute myocardial infarction; AVR, aortic valve replacement; CABG, coronary artery bypass graft; CHF, congestive heart failure; HCAHPS, Hospital Consumer Assessment of Healthcare Providers; PCI, percutaneous coronary intervention; PSI, patient safety indicators.

  • Not all measures available for all procedures; mortality or complications, not both. Major complications include complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection.

1. Prioritize elective proceduresYes22/28 at least partially electiveYes15/31 at least partially electiveYes7/8 elective
2. Include quality of life and outcome data, if possibleYesMortality for AMI and CHFYesMortality or complications*YesOutcomes for CABG, PCI, and AVR
3. Include standardized patient satisfaction and service measuresYesHCAHPSNo No 
4. Offer composite measures that are weighted and evidence‐basedNo NoSpecialty excellence award, not evidence‐basedNo 
5. Costs comparisons should include patient pricesYesAverage Medicare paymentYesCharges, health plan and Medicare costs available for a feeNo 
6. Adjust outcomes for severity and riskYesMethodology published on websiteYesMethodology not publicYesVarious methodologies published or referenced on website
7. Identify differences not due to chanceYesCompares mortality to national meanYesCompares mortality or complications to meanYesCompares mortality to national mean
8. Standardize reporting periods October 2005 to September 2006 2004‐2006 12‐24 months, ending 12/31/07 or 6/30/08
9. Avoid use of nonvalidated administrative dataYesNone usedNoUses PSIs for safety ratingYesNone used
10. Utilization rates should be evidence‐basedNoSurgical case volume of Medicare patientsNoIncludes Caesarian‐section ratesYesSome case volume rates are evidence‐based

2. Include Quality of Life and Outcome Data

Outcomes are more valuable to patients than process measures, but the risk adjustment needed to compare outcomes requires considerable effort. So far, public reporting of risk‐adjusted outcomes has been limited almost exclusively to mortality. Yet a patient contemplating knee replacement surgery would find no meaningful difference in mortalitythere were only 510 deaths nationally in year 200620but might be interested in whether patients return to full mobility after surgery, and all patients should compare rates of nosocomial infections. For some low‐risk procedures, HealthGrades Inc. (Golden, CO) includes a composite measure of major complications, including complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection; CMS will soon add rates of infection and readmission.

3. Include Measures of Patient Experience, Such as Satisfaction and Service Measures

Beyond outcomes, patients want to know about the experience of others.21 Satisfaction surveys should be standardized and made disease‐specific, since patients' experiences may differ between the cardiology suite and the delivery unit. Questions could address the attentiveness of the nursing staff, how well privacy was respected, how easy it was to deal with insurance issues, whether patients were promptly informed of test results, and whether the care team answered questions fully. Medicare has begun reporting patient satisfaction using the Hospital Consumer Assessment of Healthcare Providers (HCAHPS) survey on Hospital Compare, but the data are not disease‐specific and audit a very small number of patients from each institution. Other services are unlikely to perform their own surveys, as multiple surveys would prove burdensome. Social networking sites that allow patients to post their own personal reviews of hospitals and doctors offer an additional if less reliable dimension to traditional public reporting. Such sites are already transforming the market for other industries, such as travel.22

4. Offer Composite Measures That Are Weighted and Evidence‐Based

Interpreting multiple measures, some of which are more important than others, and some of which have better evidence than others, is difficult for health care providers and may be impossible for patients. Is it more important to get aspirin on arrival or at discharge? Also, how does a patient weigh a 1% difference in the number of heart attack patients who get aspirin on arrival against a 14% difference in those who are offered smoking cessation? Because patients may be overwhelmed by data,23 public reports should include evidence‐based, weighted measures of overall care for a given condition, with higher weights attached to those process measures most likely to have clinical benefit, and careful attention to visual representations that convey relative differences.19, 23 More sophisticated measures should be developed to guard against overuse. For example, while hospitals should be rewarded for providing vaccination, they should be penalized for vaccinating the same patient twice.

None of the services we examined provides weighted outcomes. Leapfrog (The Leapfrog Group, Washington, DC) offers a composite snapshot containing 9 pie charts, divided into 4 leaps. The 6 pies representing high‐risk procedures are of equal size, even though 2 of these, esophagectomy and pancreatic resection represent very rare surgeries, even at major medical centers. From a visual perspective, however, these are equivalent to having computerized physician order entry and full‐time intensive care unit staffing, which affect thousands more patients. Similarly, in determining pay‐for‐performance measures, CMS created a composite based on the total number of opportunities of all interventions, weighting all measures equally. Because no validated weighting measures exist, future research will be necessary to achieve this goal. Also, none of the evidence‐based measures contained safeguards against overtreatment.

5. Cost Comparisons Should Include Patient Prices

In an era of patient copayments and deductibles, consumers are increasingly aware of costs. For patients with very high deductible plans or no health insurance, hospital fees are a common cause of bankruptcy.24 Several public reporting agencies, including Hospital Compare and HealthGrades have incorporated Medicare costs into their reported measures, but these have little connection to what patients actually pay. Health sites aimed at consumers should publish the average patient copayment.

Recommendations to Ensure That Data Reflects Hospital Quality

6. Adjust Outcomes for Severity and Risk

Not all bypass operations are the same and not all patients are at equal risk. More difficult operations (eg, CABG for a patient with a previous bypass) will have more complications; similarly, patients with serious comorbidities will experience worse outcomes. Since hospitals which specialize in a procedure will attract complicated cases and higher risk patients, it is important to adjust outcomes to account for these differences. Otherwise, hospitals and surgeons may be discouraged from taking difficult cases. Outside of cardiac surgery, most risk adjustment systems use administrative claims data but vary dramatically in the numbers of variables considered and the underlying proprietary models, which are often criticized as being black boxes that yield discordant results.25 Thus, a hospital's mortality may appear below expected by 1 system and above expected by another. Instead, risk adjustment systems should include clinical data abstracted from patient records using standardized data definitions. Although costly to collect, clinical data offer more predictive information than do administrative data. For example, for heart failure patients undergoing CABG, the ejection fraction predicts mortality better than many stable comorbid diagnoses. A single transparent risk‐adjustment system should be recognized as the industry standard. The American College of Surgeons' standardized risk‐adjusted outcome reporting for the National Surgical Quality Improvement Program (NSQIP) is a good example of such an effort.

7. Identify Differences Not Due to Chance

As a result of random variation, during any period, some hospitals will appear better than average and others worse. Statistical tests should be employed to identify hospitals that differ from the mean, and to allow consumers to compare 2 hospitals directly, with appropriate caveats when the hospitals serve very different patient populations. Medicare's mortality rating system for myocardial infarction identifies only 17 hospitals in the nation as better than average and 7 as worse, out of 4,500 institutions. HealthGrades compares hospitals' actual mortality or complication rates to their predicted rates based on disease‐specific logistic regression models and reports whether the hospital is statistically better or worse than predicted. Hospitals are not compared directly to one another. Given the rarity of mortality in most procedures, other outcome measures will be necessary to distinguish among hospitals.26

8. Standardize Reporting Periods

In a world of continuous quality improvement, public reporting should represent a hospital's recent performance, but reporting periods also need to be long enough to provide a stable estimate of infrequent events, especially at low‐volume institutions. In contrast, the lag time between the end of the reporting period and public availability should be kept to a minimum. We found that reporting periods varied from 1 to 3 years, and did not always cover the same years for all conditions, even on the same website. Some data were 3 years old. Patients will have a hard time making decisions on data that is 1 year old, and hospitals will have little incentive to make improvements that will not be acknowledged for years.

9. Avoid Use of Nonvalidated Administrative Data

Administrative data collected for billing purposes, unlike most clinical data, are already in electronic format, and can inexpensively produce quality rankings using validated models.27 In contrast, screening tools, such as the Agency for Healthcare Research and Quality's patient safety indicators (PSIs), were designed to identify potential quality problems, such as postoperative deep vein thrombosis, for internal quality improvement. Cases identified by the PSI software require additional chart review,28, 29 and should not be used as quality indicators. Even so, HealthGrades reports PSIs and some insurers use them in pay‐for‐performance initiatives. Improvements in PSIs, including present‐on‐admission coding, may increase accuracy,30 but these measures need to be validated before they can be adopted for public reporting.

10. Utilization Rates Should Be Evidence‐Based

Although utilization rates for most procedures vary as much as 2‐fold by state or institution, there is little evidence for a best rate. Nevertheless, HealthGrades reports utilization rates for several obstetrical procedures. At present, there are no standards for these, and it is possible that utilization could be too low in some places. Further research is needed; until then, utilization should not purport to measure quality.

Discussion

The growing commitment to making hospital performance data public could transform the quality and safety of care in the US, introducing competition on quality and price and fostering informed consumer choice. To date, the promise of public reporting remains only partially fulfilled. Few hospitals have done more than comply with regulatory mandates and payer incentives, and consumers have failed to respond. To capture the full benefits of public reporting, we have made 10 recommendations to benefit patients and better engage hospitals. We suggest that reporting be patient‐centered, with an emphasis on making the data useful, meaningful, important, interpretable, and relevant. At the same time, hospitals, which are being judged on their performance, should have a level playing field, with measures that are timely, consistent, severity‐adjusted, evidence‐based, and which foster good clinical care. Of the 3 services we examined, Hospital Compare came closest to meeting these recommendations.

Although this blueprint for public reporting is easy to draft, it is challenging to implement. In particular, some of our suggestions, such as the one regarding risk adjustment, may not currently be feasible, because the complexity and cost of collecting clinical data, even in the era of electronic medical records, may be prohibitive. Until such data are readily available, it may be preferable to report nothing at all, rather than report data that are misleading. In the rush to make hospitals accountable, enthusiasm has often outstripped science,31 and several measures have had to be revised for unintended consequences.32

Any initiative to improve public reporting should have the buy‐in of all stakeholders, but particularly hospitals, which stand to benefit in several ways. By receiving regular feedback, they can focus on improving care, becoming better organizations. These improvements may be rewarded through direct compensation (pay‐for‐performance), decreased costs from complications, or increased market share. Hospitals will be more engaged if the data reflect actual quality, are adequately adjusted for severity, and acknowledge the role of chance. Otherwise, they will merely comply, or worse, look for opportunities to game the system. To succeed, public reporting needs to involve hospitals in establishing standards for reporting and validation, as well as auditing procedures to prevent fraud.33 The Hospital Quality Alliance (HQA, Washington, DC), a first step in this direction, at present has few measures. NSQIP (American College of Surgeons, Chicago, IL) is perhaps a better example of hospitals cooperating to set measurement standards to promote best‐practices. Public release of NSQIP data might accelerate progress. Alternatively, the National Quality Forum (NQF, Washington, DC) could expand its role from endorsing quality measures to include standardizing the way these measures are used in public reporting.

Still, if you build it, will they come? To date, public reporting has not been embraced by the public, despite its stated interest in the information. Several explanations could be offered. First, we may be presenting the wrong data. Process measures and mortality rates are important but represent abstract concepts for most patients. Surveys tell us that patients value most the experiences of other patients.14, 21 They want to know whether their pain will be controlled, whether the doctor will listen to them, whether the nurse will come when they call. The recent advent of the HCAHPS survey (AHRQ, Washington, DC) is another positive step. Stratifying the results by diagnosis and adding a few diagnosis‐specific questions would make HCAHPS even more valuable. Second, the data may not be readily available. Although most public reporting is done on the web, older patients who are deciding about hospitals may not have Internet access. Some reports are still proprietary, and cost could present another obstacle. Finally, even if freely‐available and patient‐centered, the results may not be interpretable by physicians, let alone patients.34

If public reporting is to succeed, it will require measures that better reflect patients' concerns. In order to collect the massive amounts of data required and present them in a timely fashion, better electronic record systems will be necessary. But these are no panacea; others have noted that the Department of Veterans Affairs, a leader in electronic records, still invests considerable time and money to review charts for NSQIP.35 Given the value that Americans place on transparency in other facets of their lives, it is clear that public reporting is here to stay. While much progress has been made over the past 5 years, additional research is needed to better measure quality from the patient's perspective, and to determine how this information can be used to help guide decision‐making, and to reward hospitals for offering the highest‐quality care.

Acknowledgements

The authors thank Kenneth Flax for his help with an earlier version of this manuscript.

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  18. Rothberg MB,Morsi E,Pekow PS,Benjamin EM,Lindenauer PK.Choosing the best hospital: the limitations of public reporting of hospital quality.Health Aff (Millwood).2008;27(6):16801687.
  19. Hibbard JH,Jewett JJ.Will quality report cards help consumers?Health Aff (Millwood).1997;16(3):218228.
  20. Agency for Healthcare Research and Quality. HCUPnet, Healthcare Cost and Utilization Project. Available at: http://hcupnet.ahrq.gov. Accessed January 2009.
  21. Doering LV,McGuire AW,Rourke D.Recovering from cardiac surgery: what patients want you to know.Am J Crit Care.2002;11(4):333343.
  22. Trip Advisor. Available at: http://www.tripadvisor.com. Accessed January 2009.
  23. Peters E,Dieckmann N,Dixon A,Hibbard JH,Mertz CK.Less is more in presenting quality information to consumers.Med Care Res Rev.2007;64(2):169190.
  24. Himmelstein DU,Warren E,Thorne D,Woolhandler S.MarketWatch: illness and injury as contributors to bankruptcy.Health Aff (Millwood)2005;(Suppl Web Exclusives):W5‐63W5‐73.
  25. Behal R.The Lake Wobegon effect: when all the patients are sicker.Am J Med Qual.2006;21(6):365366.
  26. Dimick JB,Welch HG,Birkmeyer JD.Surgical mortality as an indicator of hospital quality: the problem with small sample size.JAMA.2004;292(7):847851.
  27. Krumholz HM,Wang Y,Mattera JA, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113(13):16931701.
  28. Romano PS,Chan BK,Schembri ME,Rainwater JA.Can administrative data be used to compare postoperative complication rates across hospitals?Med Care.2002;40(10):856867.
  29. Naessens JM,Campbell CR,Berg B,Williams AR,Culbertson R.Impact of diagnosis‐timing indicators on measures of safety, comorbidity, and case mix groupings from administrative data sources.Med Care.2007;45(8):781788.
  30. Bahl V,Thompson MA,Kau TY,Hu HM,Campbell DA.Do the AHRQ patient safety indicators flag conditions that are present at the time of hospital admission?Med Care.2008;46(5):516522.
  31. Auerbach AD,Landefeld CS,Shojania KG.The tension between needing to improve care and knowing how to do it.N Engl J Med.2007;357(6):608613.
  32. Wachter RM,Flanders SA,Fee C,Pronovost PJ.Public reporting of antibiotic timing in patients with pneumonia: lessons from a flawed performance measure.Ann Intern Med.2008;149(1):2932.
  33. Pronovost PJ,Miller M,Wachter RM.The GAAP in quality measurement and reporting.JAMA.2007;298(15):18001802.
  34. Hibbard JH,Peters E,Dixon A,Tusler M.Consumer competencies and the use of comparative quality information: it isn't just about literacy.Med Care Res Rev.2007;64(4):379394.
  35. Hayward RA.Performance measurement in search of a path.N Engl J Med.2007;356(9):951953.
Article PDF
Issue
Journal of Hospital Medicine - 4(9)
Page Number
541-545
Legacy Keywords
medicare, public reporting, quality, risk‐adjustment
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Article PDF
Article PDF

Acknowledging striking deficiencies in the quality and safety of healthcare, the Institute of Medicine, policy makers, and payors have called for transformation of the US healthcare system.1 Public reporting of hospital performance is one key strategy for accelerating improvement2 and may improve quality in several ways. First, feedback about performance relative to peers may stimulate quality improvement activities by appealing to professionalism. Second, the desire to preserve one's reputation by not appearing on a list of poor performers may be a powerful incentive. Finally, patients and referring providers could use reports to select high‐quality hospitals, thereby shifting care from low‐quality to high‐quality hospitals and stimulating quality improvement efforts to maintain or enhance market share.

Almost 20 years after New York and Pennsylvania began reporting cardiac surgery outcomes,3 the evidence that public reporting improves healthcare quality is equivocal.4 Moreover, stakeholders have embraced public reporting to differing degrees. Public reporting does lead to greater engagement in quality improvement activities,58 and additional financial incentives provide modest incremental benefits.9 Purchasers, too, are starting to pay attention.10 In New York State, payors appear to contract more with high‐quality surgeons and avoid poorly performing outliers.11 Some payors are creating tiered systems, assigning higher patient copayments for hospitals with poor quality metrics. These new systems have not been rigorously studied and should raise concern among hospitals.12

In contrast to hospitals and payors, patients have been slow to embrace public reporting. In a survey of coronary artery bypass graft (CABG) patients in Pennsylvania, only 2% said that public reporting of mortality rates affected their decision making.13 Eight years later, only 11% of patients sought information about hospitals before deciding on elective major surgery,14 although a majority of patients in both studies expressed interest in the information. It is not clear whether recent proliferation of information on the internet will change patient behavior, but to date public reporting appears not to effect market share.5, 15, 16

Barriers to patients' use of public reporting include difficulty accessing the information, lack of trust, information that is not salient, and data that are difficult to interpret.17 In the absence of consensus on what or how to report, a growing number of organizations, including state and federal government, accrediting bodies, private foundations, and for‐profit companies report a variety of measures relating to structure, processes, and outcomes. Although these sites purport to target consumers, they sometimes offer conflicting information18 and are not easily interpreted by lay readers.19

To realize the benefits of public reporting, and minimize the unintended consequences, rating systems must report salient information in a way that is comprehensible to patients and trusted by the doctors who advise them. At the same time, they should be fair to hospitals and offer useful data for quality improvement. We offer 10 recommendations for improving the public reporting of healthcare quality information: 5 describing what to report and 5 detailing how it should be reported (Figure 1). We also examine 3 leading performance reporting programs to see how well they implement these recommendations.

Figure 1
Ten recommendations for public reporting of hospital quality.

Recommendations to Make Data Salient for Patients

1. Prioritize Elective Procedures

Hospital quality is not uniform across conditions.2 For data to be salient, then, it should be disease‐specific and focus on common elective procedures, for which consumer choice is possible. Table 1 compares 3 popular reporting services. Hospital Compare, produced by the Center for Medicare Services (CMS, US Department of Health and Human Services, Washington, DC), provides process of care measures for 4 conditions, 3 of which are not elective. The fourth, surgical infection prevention, contains 5 measures3 related to perioperative antibiotics and 2 related to thromboembolism prophylaxisfor all surgical cases. Recently, more conditions have been added, but reports are limited to the number of cases and mean Medicare charge. By year 2011, however, Hospital Compare will offer many new measures, including rates of central line infection, ventilator‐associated pneumonia, and surgical site infection. HealthGrades, a private company, offers comparative mortality rates on over 30 diagnoses, of which 15 can be considered elective, at least some of the time. Only the Leapfrog group, an industry consortium, focuses exclusively on elective procedures, offering volume measures on 7 and outcome measures on 2.

Three Popular Quality Reporting Services' Adherence to the 10 Recommendations
RuleHospital CompareHealthGradesLeapfrog
  • Abbreviations: AMI, acute myocardial infarction; AVR, aortic valve replacement; CABG, coronary artery bypass graft; CHF, congestive heart failure; HCAHPS, Hospital Consumer Assessment of Healthcare Providers; PCI, percutaneous coronary intervention; PSI, patient safety indicators.

  • Not all measures available for all procedures; mortality or complications, not both. Major complications include complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection.

1. Prioritize elective proceduresYes22/28 at least partially electiveYes15/31 at least partially electiveYes7/8 elective
2. Include quality of life and outcome data, if possibleYesMortality for AMI and CHFYesMortality or complications*YesOutcomes for CABG, PCI, and AVR
3. Include standardized patient satisfaction and service measuresYesHCAHPSNo No 
4. Offer composite measures that are weighted and evidence‐basedNo NoSpecialty excellence award, not evidence‐basedNo 
5. Costs comparisons should include patient pricesYesAverage Medicare paymentYesCharges, health plan and Medicare costs available for a feeNo 
6. Adjust outcomes for severity and riskYesMethodology published on websiteYesMethodology not publicYesVarious methodologies published or referenced on website
7. Identify differences not due to chanceYesCompares mortality to national meanYesCompares mortality or complications to meanYesCompares mortality to national mean
8. Standardize reporting periods October 2005 to September 2006 2004‐2006 12‐24 months, ending 12/31/07 or 6/30/08
9. Avoid use of nonvalidated administrative dataYesNone usedNoUses PSIs for safety ratingYesNone used
10. Utilization rates should be evidence‐basedNoSurgical case volume of Medicare patientsNoIncludes Caesarian‐section ratesYesSome case volume rates are evidence‐based

2. Include Quality of Life and Outcome Data

Outcomes are more valuable to patients than process measures, but the risk adjustment needed to compare outcomes requires considerable effort. So far, public reporting of risk‐adjusted outcomes has been limited almost exclusively to mortality. Yet a patient contemplating knee replacement surgery would find no meaningful difference in mortalitythere were only 510 deaths nationally in year 200620but might be interested in whether patients return to full mobility after surgery, and all patients should compare rates of nosocomial infections. For some low‐risk procedures, HealthGrades Inc. (Golden, CO) includes a composite measure of major complications, including complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection; CMS will soon add rates of infection and readmission.

3. Include Measures of Patient Experience, Such as Satisfaction and Service Measures

Beyond outcomes, patients want to know about the experience of others.21 Satisfaction surveys should be standardized and made disease‐specific, since patients' experiences may differ between the cardiology suite and the delivery unit. Questions could address the attentiveness of the nursing staff, how well privacy was respected, how easy it was to deal with insurance issues, whether patients were promptly informed of test results, and whether the care team answered questions fully. Medicare has begun reporting patient satisfaction using the Hospital Consumer Assessment of Healthcare Providers (HCAHPS) survey on Hospital Compare, but the data are not disease‐specific and audit a very small number of patients from each institution. Other services are unlikely to perform their own surveys, as multiple surveys would prove burdensome. Social networking sites that allow patients to post their own personal reviews of hospitals and doctors offer an additional if less reliable dimension to traditional public reporting. Such sites are already transforming the market for other industries, such as travel.22

4. Offer Composite Measures That Are Weighted and Evidence‐Based

Interpreting multiple measures, some of which are more important than others, and some of which have better evidence than others, is difficult for health care providers and may be impossible for patients. Is it more important to get aspirin on arrival or at discharge? Also, how does a patient weigh a 1% difference in the number of heart attack patients who get aspirin on arrival against a 14% difference in those who are offered smoking cessation? Because patients may be overwhelmed by data,23 public reports should include evidence‐based, weighted measures of overall care for a given condition, with higher weights attached to those process measures most likely to have clinical benefit, and careful attention to visual representations that convey relative differences.19, 23 More sophisticated measures should be developed to guard against overuse. For example, while hospitals should be rewarded for providing vaccination, they should be penalized for vaccinating the same patient twice.

None of the services we examined provides weighted outcomes. Leapfrog (The Leapfrog Group, Washington, DC) offers a composite snapshot containing 9 pie charts, divided into 4 leaps. The 6 pies representing high‐risk procedures are of equal size, even though 2 of these, esophagectomy and pancreatic resection represent very rare surgeries, even at major medical centers. From a visual perspective, however, these are equivalent to having computerized physician order entry and full‐time intensive care unit staffing, which affect thousands more patients. Similarly, in determining pay‐for‐performance measures, CMS created a composite based on the total number of opportunities of all interventions, weighting all measures equally. Because no validated weighting measures exist, future research will be necessary to achieve this goal. Also, none of the evidence‐based measures contained safeguards against overtreatment.

5. Cost Comparisons Should Include Patient Prices

In an era of patient copayments and deductibles, consumers are increasingly aware of costs. For patients with very high deductible plans or no health insurance, hospital fees are a common cause of bankruptcy.24 Several public reporting agencies, including Hospital Compare and HealthGrades have incorporated Medicare costs into their reported measures, but these have little connection to what patients actually pay. Health sites aimed at consumers should publish the average patient copayment.

Recommendations to Ensure That Data Reflects Hospital Quality

6. Adjust Outcomes for Severity and Risk

Not all bypass operations are the same and not all patients are at equal risk. More difficult operations (eg, CABG for a patient with a previous bypass) will have more complications; similarly, patients with serious comorbidities will experience worse outcomes. Since hospitals which specialize in a procedure will attract complicated cases and higher risk patients, it is important to adjust outcomes to account for these differences. Otherwise, hospitals and surgeons may be discouraged from taking difficult cases. Outside of cardiac surgery, most risk adjustment systems use administrative claims data but vary dramatically in the numbers of variables considered and the underlying proprietary models, which are often criticized as being black boxes that yield discordant results.25 Thus, a hospital's mortality may appear below expected by 1 system and above expected by another. Instead, risk adjustment systems should include clinical data abstracted from patient records using standardized data definitions. Although costly to collect, clinical data offer more predictive information than do administrative data. For example, for heart failure patients undergoing CABG, the ejection fraction predicts mortality better than many stable comorbid diagnoses. A single transparent risk‐adjustment system should be recognized as the industry standard. The American College of Surgeons' standardized risk‐adjusted outcome reporting for the National Surgical Quality Improvement Program (NSQIP) is a good example of such an effort.

7. Identify Differences Not Due to Chance

As a result of random variation, during any period, some hospitals will appear better than average and others worse. Statistical tests should be employed to identify hospitals that differ from the mean, and to allow consumers to compare 2 hospitals directly, with appropriate caveats when the hospitals serve very different patient populations. Medicare's mortality rating system for myocardial infarction identifies only 17 hospitals in the nation as better than average and 7 as worse, out of 4,500 institutions. HealthGrades compares hospitals' actual mortality or complication rates to their predicted rates based on disease‐specific logistic regression models and reports whether the hospital is statistically better or worse than predicted. Hospitals are not compared directly to one another. Given the rarity of mortality in most procedures, other outcome measures will be necessary to distinguish among hospitals.26

8. Standardize Reporting Periods

In a world of continuous quality improvement, public reporting should represent a hospital's recent performance, but reporting periods also need to be long enough to provide a stable estimate of infrequent events, especially at low‐volume institutions. In contrast, the lag time between the end of the reporting period and public availability should be kept to a minimum. We found that reporting periods varied from 1 to 3 years, and did not always cover the same years for all conditions, even on the same website. Some data were 3 years old. Patients will have a hard time making decisions on data that is 1 year old, and hospitals will have little incentive to make improvements that will not be acknowledged for years.

9. Avoid Use of Nonvalidated Administrative Data

Administrative data collected for billing purposes, unlike most clinical data, are already in electronic format, and can inexpensively produce quality rankings using validated models.27 In contrast, screening tools, such as the Agency for Healthcare Research and Quality's patient safety indicators (PSIs), were designed to identify potential quality problems, such as postoperative deep vein thrombosis, for internal quality improvement. Cases identified by the PSI software require additional chart review,28, 29 and should not be used as quality indicators. Even so, HealthGrades reports PSIs and some insurers use them in pay‐for‐performance initiatives. Improvements in PSIs, including present‐on‐admission coding, may increase accuracy,30 but these measures need to be validated before they can be adopted for public reporting.

10. Utilization Rates Should Be Evidence‐Based

Although utilization rates for most procedures vary as much as 2‐fold by state or institution, there is little evidence for a best rate. Nevertheless, HealthGrades reports utilization rates for several obstetrical procedures. At present, there are no standards for these, and it is possible that utilization could be too low in some places. Further research is needed; until then, utilization should not purport to measure quality.

Discussion

The growing commitment to making hospital performance data public could transform the quality and safety of care in the US, introducing competition on quality and price and fostering informed consumer choice. To date, the promise of public reporting remains only partially fulfilled. Few hospitals have done more than comply with regulatory mandates and payer incentives, and consumers have failed to respond. To capture the full benefits of public reporting, we have made 10 recommendations to benefit patients and better engage hospitals. We suggest that reporting be patient‐centered, with an emphasis on making the data useful, meaningful, important, interpretable, and relevant. At the same time, hospitals, which are being judged on their performance, should have a level playing field, with measures that are timely, consistent, severity‐adjusted, evidence‐based, and which foster good clinical care. Of the 3 services we examined, Hospital Compare came closest to meeting these recommendations.

Although this blueprint for public reporting is easy to draft, it is challenging to implement. In particular, some of our suggestions, such as the one regarding risk adjustment, may not currently be feasible, because the complexity and cost of collecting clinical data, even in the era of electronic medical records, may be prohibitive. Until such data are readily available, it may be preferable to report nothing at all, rather than report data that are misleading. In the rush to make hospitals accountable, enthusiasm has often outstripped science,31 and several measures have had to be revised for unintended consequences.32

Any initiative to improve public reporting should have the buy‐in of all stakeholders, but particularly hospitals, which stand to benefit in several ways. By receiving regular feedback, they can focus on improving care, becoming better organizations. These improvements may be rewarded through direct compensation (pay‐for‐performance), decreased costs from complications, or increased market share. Hospitals will be more engaged if the data reflect actual quality, are adequately adjusted for severity, and acknowledge the role of chance. Otherwise, they will merely comply, or worse, look for opportunities to game the system. To succeed, public reporting needs to involve hospitals in establishing standards for reporting and validation, as well as auditing procedures to prevent fraud.33 The Hospital Quality Alliance (HQA, Washington, DC), a first step in this direction, at present has few measures. NSQIP (American College of Surgeons, Chicago, IL) is perhaps a better example of hospitals cooperating to set measurement standards to promote best‐practices. Public release of NSQIP data might accelerate progress. Alternatively, the National Quality Forum (NQF, Washington, DC) could expand its role from endorsing quality measures to include standardizing the way these measures are used in public reporting.

Still, if you build it, will they come? To date, public reporting has not been embraced by the public, despite its stated interest in the information. Several explanations could be offered. First, we may be presenting the wrong data. Process measures and mortality rates are important but represent abstract concepts for most patients. Surveys tell us that patients value most the experiences of other patients.14, 21 They want to know whether their pain will be controlled, whether the doctor will listen to them, whether the nurse will come when they call. The recent advent of the HCAHPS survey (AHRQ, Washington, DC) is another positive step. Stratifying the results by diagnosis and adding a few diagnosis‐specific questions would make HCAHPS even more valuable. Second, the data may not be readily available. Although most public reporting is done on the web, older patients who are deciding about hospitals may not have Internet access. Some reports are still proprietary, and cost could present another obstacle. Finally, even if freely‐available and patient‐centered, the results may not be interpretable by physicians, let alone patients.34

If public reporting is to succeed, it will require measures that better reflect patients' concerns. In order to collect the massive amounts of data required and present them in a timely fashion, better electronic record systems will be necessary. But these are no panacea; others have noted that the Department of Veterans Affairs, a leader in electronic records, still invests considerable time and money to review charts for NSQIP.35 Given the value that Americans place on transparency in other facets of their lives, it is clear that public reporting is here to stay. While much progress has been made over the past 5 years, additional research is needed to better measure quality from the patient's perspective, and to determine how this information can be used to help guide decision‐making, and to reward hospitals for offering the highest‐quality care.

Acknowledgements

The authors thank Kenneth Flax for his help with an earlier version of this manuscript.

Acknowledging striking deficiencies in the quality and safety of healthcare, the Institute of Medicine, policy makers, and payors have called for transformation of the US healthcare system.1 Public reporting of hospital performance is one key strategy for accelerating improvement2 and may improve quality in several ways. First, feedback about performance relative to peers may stimulate quality improvement activities by appealing to professionalism. Second, the desire to preserve one's reputation by not appearing on a list of poor performers may be a powerful incentive. Finally, patients and referring providers could use reports to select high‐quality hospitals, thereby shifting care from low‐quality to high‐quality hospitals and stimulating quality improvement efforts to maintain or enhance market share.

Almost 20 years after New York and Pennsylvania began reporting cardiac surgery outcomes,3 the evidence that public reporting improves healthcare quality is equivocal.4 Moreover, stakeholders have embraced public reporting to differing degrees. Public reporting does lead to greater engagement in quality improvement activities,58 and additional financial incentives provide modest incremental benefits.9 Purchasers, too, are starting to pay attention.10 In New York State, payors appear to contract more with high‐quality surgeons and avoid poorly performing outliers.11 Some payors are creating tiered systems, assigning higher patient copayments for hospitals with poor quality metrics. These new systems have not been rigorously studied and should raise concern among hospitals.12

In contrast to hospitals and payors, patients have been slow to embrace public reporting. In a survey of coronary artery bypass graft (CABG) patients in Pennsylvania, only 2% said that public reporting of mortality rates affected their decision making.13 Eight years later, only 11% of patients sought information about hospitals before deciding on elective major surgery,14 although a majority of patients in both studies expressed interest in the information. It is not clear whether recent proliferation of information on the internet will change patient behavior, but to date public reporting appears not to effect market share.5, 15, 16

Barriers to patients' use of public reporting include difficulty accessing the information, lack of trust, information that is not salient, and data that are difficult to interpret.17 In the absence of consensus on what or how to report, a growing number of organizations, including state and federal government, accrediting bodies, private foundations, and for‐profit companies report a variety of measures relating to structure, processes, and outcomes. Although these sites purport to target consumers, they sometimes offer conflicting information18 and are not easily interpreted by lay readers.19

To realize the benefits of public reporting, and minimize the unintended consequences, rating systems must report salient information in a way that is comprehensible to patients and trusted by the doctors who advise them. At the same time, they should be fair to hospitals and offer useful data for quality improvement. We offer 10 recommendations for improving the public reporting of healthcare quality information: 5 describing what to report and 5 detailing how it should be reported (Figure 1). We also examine 3 leading performance reporting programs to see how well they implement these recommendations.

Figure 1
Ten recommendations for public reporting of hospital quality.

Recommendations to Make Data Salient for Patients

1. Prioritize Elective Procedures

Hospital quality is not uniform across conditions.2 For data to be salient, then, it should be disease‐specific and focus on common elective procedures, for which consumer choice is possible. Table 1 compares 3 popular reporting services. Hospital Compare, produced by the Center for Medicare Services (CMS, US Department of Health and Human Services, Washington, DC), provides process of care measures for 4 conditions, 3 of which are not elective. The fourth, surgical infection prevention, contains 5 measures3 related to perioperative antibiotics and 2 related to thromboembolism prophylaxisfor all surgical cases. Recently, more conditions have been added, but reports are limited to the number of cases and mean Medicare charge. By year 2011, however, Hospital Compare will offer many new measures, including rates of central line infection, ventilator‐associated pneumonia, and surgical site infection. HealthGrades, a private company, offers comparative mortality rates on over 30 diagnoses, of which 15 can be considered elective, at least some of the time. Only the Leapfrog group, an industry consortium, focuses exclusively on elective procedures, offering volume measures on 7 and outcome measures on 2.

Three Popular Quality Reporting Services' Adherence to the 10 Recommendations
RuleHospital CompareHealthGradesLeapfrog
  • Abbreviations: AMI, acute myocardial infarction; AVR, aortic valve replacement; CABG, coronary artery bypass graft; CHF, congestive heart failure; HCAHPS, Hospital Consumer Assessment of Healthcare Providers; PCI, percutaneous coronary intervention; PSI, patient safety indicators.

  • Not all measures available for all procedures; mortality or complications, not both. Major complications include complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection.

1. Prioritize elective proceduresYes22/28 at least partially electiveYes15/31 at least partially electiveYes7/8 elective
2. Include quality of life and outcome data, if possibleYesMortality for AMI and CHFYesMortality or complications*YesOutcomes for CABG, PCI, and AVR
3. Include standardized patient satisfaction and service measuresYesHCAHPSNo No 
4. Offer composite measures that are weighted and evidence‐basedNo NoSpecialty excellence award, not evidence‐basedNo 
5. Costs comparisons should include patient pricesYesAverage Medicare paymentYesCharges, health plan and Medicare costs available for a feeNo 
6. Adjust outcomes for severity and riskYesMethodology published on websiteYesMethodology not publicYesVarious methodologies published or referenced on website
7. Identify differences not due to chanceYesCompares mortality to national meanYesCompares mortality or complications to meanYesCompares mortality to national mean
8. Standardize reporting periods October 2005 to September 2006 2004‐2006 12‐24 months, ending 12/31/07 or 6/30/08
9. Avoid use of nonvalidated administrative dataYesNone usedNoUses PSIs for safety ratingYesNone used
10. Utilization rates should be evidence‐basedNoSurgical case volume of Medicare patientsNoIncludes Caesarian‐section ratesYesSome case volume rates are evidence‐based

2. Include Quality of Life and Outcome Data

Outcomes are more valuable to patients than process measures, but the risk adjustment needed to compare outcomes requires considerable effort. So far, public reporting of risk‐adjusted outcomes has been limited almost exclusively to mortality. Yet a patient contemplating knee replacement surgery would find no meaningful difference in mortalitythere were only 510 deaths nationally in year 200620but might be interested in whether patients return to full mobility after surgery, and all patients should compare rates of nosocomial infections. For some low‐risk procedures, HealthGrades Inc. (Golden, CO) includes a composite measure of major complications, including complication of an orthopedic implant, stroke, cardiac arrest, excessive bleeding, and some types of infection; CMS will soon add rates of infection and readmission.

3. Include Measures of Patient Experience, Such as Satisfaction and Service Measures

Beyond outcomes, patients want to know about the experience of others.21 Satisfaction surveys should be standardized and made disease‐specific, since patients' experiences may differ between the cardiology suite and the delivery unit. Questions could address the attentiveness of the nursing staff, how well privacy was respected, how easy it was to deal with insurance issues, whether patients were promptly informed of test results, and whether the care team answered questions fully. Medicare has begun reporting patient satisfaction using the Hospital Consumer Assessment of Healthcare Providers (HCAHPS) survey on Hospital Compare, but the data are not disease‐specific and audit a very small number of patients from each institution. Other services are unlikely to perform their own surveys, as multiple surveys would prove burdensome. Social networking sites that allow patients to post their own personal reviews of hospitals and doctors offer an additional if less reliable dimension to traditional public reporting. Such sites are already transforming the market for other industries, such as travel.22

4. Offer Composite Measures That Are Weighted and Evidence‐Based

Interpreting multiple measures, some of which are more important than others, and some of which have better evidence than others, is difficult for health care providers and may be impossible for patients. Is it more important to get aspirin on arrival or at discharge? Also, how does a patient weigh a 1% difference in the number of heart attack patients who get aspirin on arrival against a 14% difference in those who are offered smoking cessation? Because patients may be overwhelmed by data,23 public reports should include evidence‐based, weighted measures of overall care for a given condition, with higher weights attached to those process measures most likely to have clinical benefit, and careful attention to visual representations that convey relative differences.19, 23 More sophisticated measures should be developed to guard against overuse. For example, while hospitals should be rewarded for providing vaccination, they should be penalized for vaccinating the same patient twice.

None of the services we examined provides weighted outcomes. Leapfrog (The Leapfrog Group, Washington, DC) offers a composite snapshot containing 9 pie charts, divided into 4 leaps. The 6 pies representing high‐risk procedures are of equal size, even though 2 of these, esophagectomy and pancreatic resection represent very rare surgeries, even at major medical centers. From a visual perspective, however, these are equivalent to having computerized physician order entry and full‐time intensive care unit staffing, which affect thousands more patients. Similarly, in determining pay‐for‐performance measures, CMS created a composite based on the total number of opportunities of all interventions, weighting all measures equally. Because no validated weighting measures exist, future research will be necessary to achieve this goal. Also, none of the evidence‐based measures contained safeguards against overtreatment.

5. Cost Comparisons Should Include Patient Prices

In an era of patient copayments and deductibles, consumers are increasingly aware of costs. For patients with very high deductible plans or no health insurance, hospital fees are a common cause of bankruptcy.24 Several public reporting agencies, including Hospital Compare and HealthGrades have incorporated Medicare costs into their reported measures, but these have little connection to what patients actually pay. Health sites aimed at consumers should publish the average patient copayment.

Recommendations to Ensure That Data Reflects Hospital Quality

6. Adjust Outcomes for Severity and Risk

Not all bypass operations are the same and not all patients are at equal risk. More difficult operations (eg, CABG for a patient with a previous bypass) will have more complications; similarly, patients with serious comorbidities will experience worse outcomes. Since hospitals which specialize in a procedure will attract complicated cases and higher risk patients, it is important to adjust outcomes to account for these differences. Otherwise, hospitals and surgeons may be discouraged from taking difficult cases. Outside of cardiac surgery, most risk adjustment systems use administrative claims data but vary dramatically in the numbers of variables considered and the underlying proprietary models, which are often criticized as being black boxes that yield discordant results.25 Thus, a hospital's mortality may appear below expected by 1 system and above expected by another. Instead, risk adjustment systems should include clinical data abstracted from patient records using standardized data definitions. Although costly to collect, clinical data offer more predictive information than do administrative data. For example, for heart failure patients undergoing CABG, the ejection fraction predicts mortality better than many stable comorbid diagnoses. A single transparent risk‐adjustment system should be recognized as the industry standard. The American College of Surgeons' standardized risk‐adjusted outcome reporting for the National Surgical Quality Improvement Program (NSQIP) is a good example of such an effort.

7. Identify Differences Not Due to Chance

As a result of random variation, during any period, some hospitals will appear better than average and others worse. Statistical tests should be employed to identify hospitals that differ from the mean, and to allow consumers to compare 2 hospitals directly, with appropriate caveats when the hospitals serve very different patient populations. Medicare's mortality rating system for myocardial infarction identifies only 17 hospitals in the nation as better than average and 7 as worse, out of 4,500 institutions. HealthGrades compares hospitals' actual mortality or complication rates to their predicted rates based on disease‐specific logistic regression models and reports whether the hospital is statistically better or worse than predicted. Hospitals are not compared directly to one another. Given the rarity of mortality in most procedures, other outcome measures will be necessary to distinguish among hospitals.26

8. Standardize Reporting Periods

In a world of continuous quality improvement, public reporting should represent a hospital's recent performance, but reporting periods also need to be long enough to provide a stable estimate of infrequent events, especially at low‐volume institutions. In contrast, the lag time between the end of the reporting period and public availability should be kept to a minimum. We found that reporting periods varied from 1 to 3 years, and did not always cover the same years for all conditions, even on the same website. Some data were 3 years old. Patients will have a hard time making decisions on data that is 1 year old, and hospitals will have little incentive to make improvements that will not be acknowledged for years.

9. Avoid Use of Nonvalidated Administrative Data

Administrative data collected for billing purposes, unlike most clinical data, are already in electronic format, and can inexpensively produce quality rankings using validated models.27 In contrast, screening tools, such as the Agency for Healthcare Research and Quality's patient safety indicators (PSIs), were designed to identify potential quality problems, such as postoperative deep vein thrombosis, for internal quality improvement. Cases identified by the PSI software require additional chart review,28, 29 and should not be used as quality indicators. Even so, HealthGrades reports PSIs and some insurers use them in pay‐for‐performance initiatives. Improvements in PSIs, including present‐on‐admission coding, may increase accuracy,30 but these measures need to be validated before they can be adopted for public reporting.

10. Utilization Rates Should Be Evidence‐Based

Although utilization rates for most procedures vary as much as 2‐fold by state or institution, there is little evidence for a best rate. Nevertheless, HealthGrades reports utilization rates for several obstetrical procedures. At present, there are no standards for these, and it is possible that utilization could be too low in some places. Further research is needed; until then, utilization should not purport to measure quality.

Discussion

The growing commitment to making hospital performance data public could transform the quality and safety of care in the US, introducing competition on quality and price and fostering informed consumer choice. To date, the promise of public reporting remains only partially fulfilled. Few hospitals have done more than comply with regulatory mandates and payer incentives, and consumers have failed to respond. To capture the full benefits of public reporting, we have made 10 recommendations to benefit patients and better engage hospitals. We suggest that reporting be patient‐centered, with an emphasis on making the data useful, meaningful, important, interpretable, and relevant. At the same time, hospitals, which are being judged on their performance, should have a level playing field, with measures that are timely, consistent, severity‐adjusted, evidence‐based, and which foster good clinical care. Of the 3 services we examined, Hospital Compare came closest to meeting these recommendations.

Although this blueprint for public reporting is easy to draft, it is challenging to implement. In particular, some of our suggestions, such as the one regarding risk adjustment, may not currently be feasible, because the complexity and cost of collecting clinical data, even in the era of electronic medical records, may be prohibitive. Until such data are readily available, it may be preferable to report nothing at all, rather than report data that are misleading. In the rush to make hospitals accountable, enthusiasm has often outstripped science,31 and several measures have had to be revised for unintended consequences.32

Any initiative to improve public reporting should have the buy‐in of all stakeholders, but particularly hospitals, which stand to benefit in several ways. By receiving regular feedback, they can focus on improving care, becoming better organizations. These improvements may be rewarded through direct compensation (pay‐for‐performance), decreased costs from complications, or increased market share. Hospitals will be more engaged if the data reflect actual quality, are adequately adjusted for severity, and acknowledge the role of chance. Otherwise, they will merely comply, or worse, look for opportunities to game the system. To succeed, public reporting needs to involve hospitals in establishing standards for reporting and validation, as well as auditing procedures to prevent fraud.33 The Hospital Quality Alliance (HQA, Washington, DC), a first step in this direction, at present has few measures. NSQIP (American College of Surgeons, Chicago, IL) is perhaps a better example of hospitals cooperating to set measurement standards to promote best‐practices. Public release of NSQIP data might accelerate progress. Alternatively, the National Quality Forum (NQF, Washington, DC) could expand its role from endorsing quality measures to include standardizing the way these measures are used in public reporting.

Still, if you build it, will they come? To date, public reporting has not been embraced by the public, despite its stated interest in the information. Several explanations could be offered. First, we may be presenting the wrong data. Process measures and mortality rates are important but represent abstract concepts for most patients. Surveys tell us that patients value most the experiences of other patients.14, 21 They want to know whether their pain will be controlled, whether the doctor will listen to them, whether the nurse will come when they call. The recent advent of the HCAHPS survey (AHRQ, Washington, DC) is another positive step. Stratifying the results by diagnosis and adding a few diagnosis‐specific questions would make HCAHPS even more valuable. Second, the data may not be readily available. Although most public reporting is done on the web, older patients who are deciding about hospitals may not have Internet access. Some reports are still proprietary, and cost could present another obstacle. Finally, even if freely‐available and patient‐centered, the results may not be interpretable by physicians, let alone patients.34

If public reporting is to succeed, it will require measures that better reflect patients' concerns. In order to collect the massive amounts of data required and present them in a timely fashion, better electronic record systems will be necessary. But these are no panacea; others have noted that the Department of Veterans Affairs, a leader in electronic records, still invests considerable time and money to review charts for NSQIP.35 Given the value that Americans place on transparency in other facets of their lives, it is clear that public reporting is here to stay. While much progress has been made over the past 5 years, additional research is needed to better measure quality from the patient's perspective, and to determine how this information can be used to help guide decision‐making, and to reward hospitals for offering the highest‐quality care.

Acknowledgements

The authors thank Kenneth Flax for his help with an earlier version of this manuscript.

References
  1. Committee on Quality of Health Care in America IoM.Crossing the Quality Chasm: A New Health System for the 21st Century.Washington, DC:National Academy Press;2001.
  2. Jha AK,Li Z,Orav EJ,Epstein AM.Care in U.S. hospitals: the Hospital Quality Alliance program.N Engl J Med.2005;353(3):265274.
  3. Chassin MR.Achieving and sustaining improved quality: lessons from New York state and cardiac surgery.Health Aff. 20022002;21(4):4051.
  4. Fung CH,Lim Y‐W,Mattke S,Damberg C,Shekelle PG.Systematic review: the evidence that publishing patient care performance data improves quality of care.Ann Intern Med.2008;148(2):111123.
  5. Hibbard JH,Stockard J,Tusler M.Hospital performance reports: impact on quality, market share, and reputation.Health Aff (Millwood).2005;24(4):11501160.
  6. Hibbard JH,Stockard J,Tusler M.Does publicizing hospital performance stimulate quality improvement efforts?Health Aff (Millwood).2003;22(2):8494.
  7. Hannan EL,Kilburn H,Racz M,Shields E,Chassin MR.Improving the outcomes of coronary artery bypass surgery in New York State.JAMA.1994;271(10):761766.
  8. Rosenthal GE,Quinn L,Harper DL.Declines in hospital mortality associated with a regional initiative to measure hospital performance.Am J Med Qual.1997;12(2):103112.
  9. Lindenauer PK,Remus D,Roman S, et al.Public reporting and pay for performance in hospital quality improvement.N Engl J Med.2007;356(5):486496.
  10. Mukamel DB,Mushlin AI,Weimer D,Zwanziger J,Parker T,Indridason I.Do quality report cards play a role in HMOs' contracting practices? Evidence from New York State.Health Serv Res.2000;35(1 Pt 2):319332.
  11. Mukamel DB,Weimer DL,Zwanziger J,Mushlin AI.Quality of cardiac surgeons and managed care contracting practices.Health Serv Res.2002;37(5):11291144.
  12. Rosenthal MB,Landrum MB,Meara E,Huskamp HA,Conti RM,Keating NL.Using performance data to identify preferred hospitals.Health Serv Res.2007;42(6 Pt 1):21092119; discussion 2294–2323.
  13. Schneider EC,Epstein AM.Use of public performance reports: a survey of patients undergoing cardiac surgery.JAMA.1998;279(20):16381642.
  14. Schwartz LM,Woloshin S,Birkmeyer JD.How do elderly patients decide where to go for major surgery? Telephone interview survey.BMJ.2005;331(7520):821.
  15. Baker DW,Einstadter D,Thomas C,Husak S,Gordon NH,Cebul RD.The effect of publicly reporting hospital performance on market share and risk‐adjusted mortality at high‐mortality hospitals.Med Care.2003;41(6):729740.
  16. Jha AK,Epstein AM.The predictive accuracy of the New York State coronary artery bypass surgery report‐card system.Health Aff (Millwood).2006;25(3):844855.
  17. Schneider EC,Lieberman T.Publicly disclosed information about the quality of health care: response of the US public.Qual Saf Health Care.2001;10(2):96103.
  18. Rothberg MB,Morsi E,Pekow PS,Benjamin EM,Lindenauer PK.Choosing the best hospital: the limitations of public reporting of hospital quality.Health Aff (Millwood).2008;27(6):16801687.
  19. Hibbard JH,Jewett JJ.Will quality report cards help consumers?Health Aff (Millwood).1997;16(3):218228.
  20. Agency for Healthcare Research and Quality. HCUPnet, Healthcare Cost and Utilization Project. Available at: http://hcupnet.ahrq.gov. Accessed January 2009.
  21. Doering LV,McGuire AW,Rourke D.Recovering from cardiac surgery: what patients want you to know.Am J Crit Care.2002;11(4):333343.
  22. Trip Advisor. Available at: http://www.tripadvisor.com. Accessed January 2009.
  23. Peters E,Dieckmann N,Dixon A,Hibbard JH,Mertz CK.Less is more in presenting quality information to consumers.Med Care Res Rev.2007;64(2):169190.
  24. Himmelstein DU,Warren E,Thorne D,Woolhandler S.MarketWatch: illness and injury as contributors to bankruptcy.Health Aff (Millwood)2005;(Suppl Web Exclusives):W5‐63W5‐73.
  25. Behal R.The Lake Wobegon effect: when all the patients are sicker.Am J Med Qual.2006;21(6):365366.
  26. Dimick JB,Welch HG,Birkmeyer JD.Surgical mortality as an indicator of hospital quality: the problem with small sample size.JAMA.2004;292(7):847851.
  27. Krumholz HM,Wang Y,Mattera JA, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113(13):16931701.
  28. Romano PS,Chan BK,Schembri ME,Rainwater JA.Can administrative data be used to compare postoperative complication rates across hospitals?Med Care.2002;40(10):856867.
  29. Naessens JM,Campbell CR,Berg B,Williams AR,Culbertson R.Impact of diagnosis‐timing indicators on measures of safety, comorbidity, and case mix groupings from administrative data sources.Med Care.2007;45(8):781788.
  30. Bahl V,Thompson MA,Kau TY,Hu HM,Campbell DA.Do the AHRQ patient safety indicators flag conditions that are present at the time of hospital admission?Med Care.2008;46(5):516522.
  31. Auerbach AD,Landefeld CS,Shojania KG.The tension between needing to improve care and knowing how to do it.N Engl J Med.2007;357(6):608613.
  32. Wachter RM,Flanders SA,Fee C,Pronovost PJ.Public reporting of antibiotic timing in patients with pneumonia: lessons from a flawed performance measure.Ann Intern Med.2008;149(1):2932.
  33. Pronovost PJ,Miller M,Wachter RM.The GAAP in quality measurement and reporting.JAMA.2007;298(15):18001802.
  34. Hibbard JH,Peters E,Dixon A,Tusler M.Consumer competencies and the use of comparative quality information: it isn't just about literacy.Med Care Res Rev.2007;64(4):379394.
  35. Hayward RA.Performance measurement in search of a path.N Engl J Med.2007;356(9):951953.
References
  1. Committee on Quality of Health Care in America IoM.Crossing the Quality Chasm: A New Health System for the 21st Century.Washington, DC:National Academy Press;2001.
  2. Jha AK,Li Z,Orav EJ,Epstein AM.Care in U.S. hospitals: the Hospital Quality Alliance program.N Engl J Med.2005;353(3):265274.
  3. Chassin MR.Achieving and sustaining improved quality: lessons from New York state and cardiac surgery.Health Aff. 20022002;21(4):4051.
  4. Fung CH,Lim Y‐W,Mattke S,Damberg C,Shekelle PG.Systematic review: the evidence that publishing patient care performance data improves quality of care.Ann Intern Med.2008;148(2):111123.
  5. Hibbard JH,Stockard J,Tusler M.Hospital performance reports: impact on quality, market share, and reputation.Health Aff (Millwood).2005;24(4):11501160.
  6. Hibbard JH,Stockard J,Tusler M.Does publicizing hospital performance stimulate quality improvement efforts?Health Aff (Millwood).2003;22(2):8494.
  7. Hannan EL,Kilburn H,Racz M,Shields E,Chassin MR.Improving the outcomes of coronary artery bypass surgery in New York State.JAMA.1994;271(10):761766.
  8. Rosenthal GE,Quinn L,Harper DL.Declines in hospital mortality associated with a regional initiative to measure hospital performance.Am J Med Qual.1997;12(2):103112.
  9. Lindenauer PK,Remus D,Roman S, et al.Public reporting and pay for performance in hospital quality improvement.N Engl J Med.2007;356(5):486496.
  10. Mukamel DB,Mushlin AI,Weimer D,Zwanziger J,Parker T,Indridason I.Do quality report cards play a role in HMOs' contracting practices? Evidence from New York State.Health Serv Res.2000;35(1 Pt 2):319332.
  11. Mukamel DB,Weimer DL,Zwanziger J,Mushlin AI.Quality of cardiac surgeons and managed care contracting practices.Health Serv Res.2002;37(5):11291144.
  12. Rosenthal MB,Landrum MB,Meara E,Huskamp HA,Conti RM,Keating NL.Using performance data to identify preferred hospitals.Health Serv Res.2007;42(6 Pt 1):21092119; discussion 2294–2323.
  13. Schneider EC,Epstein AM.Use of public performance reports: a survey of patients undergoing cardiac surgery.JAMA.1998;279(20):16381642.
  14. Schwartz LM,Woloshin S,Birkmeyer JD.How do elderly patients decide where to go for major surgery? Telephone interview survey.BMJ.2005;331(7520):821.
  15. Baker DW,Einstadter D,Thomas C,Husak S,Gordon NH,Cebul RD.The effect of publicly reporting hospital performance on market share and risk‐adjusted mortality at high‐mortality hospitals.Med Care.2003;41(6):729740.
  16. Jha AK,Epstein AM.The predictive accuracy of the New York State coronary artery bypass surgery report‐card system.Health Aff (Millwood).2006;25(3):844855.
  17. Schneider EC,Lieberman T.Publicly disclosed information about the quality of health care: response of the US public.Qual Saf Health Care.2001;10(2):96103.
  18. Rothberg MB,Morsi E,Pekow PS,Benjamin EM,Lindenauer PK.Choosing the best hospital: the limitations of public reporting of hospital quality.Health Aff (Millwood).2008;27(6):16801687.
  19. Hibbard JH,Jewett JJ.Will quality report cards help consumers?Health Aff (Millwood).1997;16(3):218228.
  20. Agency for Healthcare Research and Quality. HCUPnet, Healthcare Cost and Utilization Project. Available at: http://hcupnet.ahrq.gov. Accessed January 2009.
  21. Doering LV,McGuire AW,Rourke D.Recovering from cardiac surgery: what patients want you to know.Am J Crit Care.2002;11(4):333343.
  22. Trip Advisor. Available at: http://www.tripadvisor.com. Accessed January 2009.
  23. Peters E,Dieckmann N,Dixon A,Hibbard JH,Mertz CK.Less is more in presenting quality information to consumers.Med Care Res Rev.2007;64(2):169190.
  24. Himmelstein DU,Warren E,Thorne D,Woolhandler S.MarketWatch: illness and injury as contributors to bankruptcy.Health Aff (Millwood)2005;(Suppl Web Exclusives):W5‐63W5‐73.
  25. Behal R.The Lake Wobegon effect: when all the patients are sicker.Am J Med Qual.2006;21(6):365366.
  26. Dimick JB,Welch HG,Birkmeyer JD.Surgical mortality as an indicator of hospital quality: the problem with small sample size.JAMA.2004;292(7):847851.
  27. Krumholz HM,Wang Y,Mattera JA, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113(13):16931701.
  28. Romano PS,Chan BK,Schembri ME,Rainwater JA.Can administrative data be used to compare postoperative complication rates across hospitals?Med Care.2002;40(10):856867.
  29. Naessens JM,Campbell CR,Berg B,Williams AR,Culbertson R.Impact of diagnosis‐timing indicators on measures of safety, comorbidity, and case mix groupings from administrative data sources.Med Care.2007;45(8):781788.
  30. Bahl V,Thompson MA,Kau TY,Hu HM,Campbell DA.Do the AHRQ patient safety indicators flag conditions that are present at the time of hospital admission?Med Care.2008;46(5):516522.
  31. Auerbach AD,Landefeld CS,Shojania KG.The tension between needing to improve care and knowing how to do it.N Engl J Med.2007;357(6):608613.
  32. Wachter RM,Flanders SA,Fee C,Pronovost PJ.Public reporting of antibiotic timing in patients with pneumonia: lessons from a flawed performance measure.Ann Intern Med.2008;149(1):2932.
  33. Pronovost PJ,Miller M,Wachter RM.The GAAP in quality measurement and reporting.JAMA.2007;298(15):18001802.
  34. Hibbard JH,Peters E,Dixon A,Tusler M.Consumer competencies and the use of comparative quality information: it isn't just about literacy.Med Care Res Rev.2007;64(4):379394.
  35. Hayward RA.Performance measurement in search of a path.N Engl J Med.2007;356(9):951953.
Issue
Journal of Hospital Medicine - 4(9)
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Journal of Hospital Medicine - 4(9)
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541-545
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541-545
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Public reporting of hospital quality: Recommendations to benefit patients and hospitals
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Public reporting of hospital quality: Recommendations to benefit patients and hospitals
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medicare, public reporting, quality, risk‐adjustment
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medicare, public reporting, quality, risk‐adjustment
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Acute Vertebral Fracture

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Acute vertebral fracture

An 89‐year‐old female presents to the Emergency Department with lower back pain for the past 5 days. The patient has a past medical history of polymyalgia rheumatica and hypothyroidism. Her medications include prednisone 10 mg daily and levothyroxine 50 g daily. Aside from tenderness over the third lumbar vertebra, her physical exam is unremarkable. An x‐ray shows a fracture of the third lumbar vertebra. Basic laboratory studies, including calcium and creatinine are normal.

The Clinical Problem and Impact

Vertebral fractures (often termed vertebral compression fractures) affect approximately 25% of all postmenopausal women.1, 2 Only one‐third of vertebral fractures are brought to medical attention.3, 4 In the remaining two‐thirds, patients are either asymptomatic or do not seek medical attention. The lifetime risk of a clinical vertebral fracture is approximately 16% and 5% in white women and men, respectively.1 The risk of vertebral fracture increases with age, lower bone mineral density (BMD), and prior vertebral fracture.1, 5 Women with a preexisting vertebral fracture have a 5‐fold increased risk for a new vertebral fracture relative to those without a history of vertebral fracture.5, 6 Approximately 20% of women who sustain a vertebral fracture will have a new vertebral fracture in the subsequent year.5, 6 Vertebral fractures are frequently overlooked on chest x‐rays and hence there is a need for increased awareness and improved recognition of radiographically‐demonstrated fractures.7 Hospitalists must appreciate that a vertebral fracture is often the first clue to underlying osteoporosis. At least 90% of vertebral and hip fractures are attributable to osteoporosis.5

Typically, the pain related to an acute vertebral fracture improves over 4 to 6 weeks.8, 9 However, pain can persist, resulting in functional impairment, and a decline in the quality of life.1013 Vertebral fractures may lead to kyphosis and reduced lung function. This, in turn, may increase the risk for pneumonia, the most common cause of death in patients with osteoporosis.4 Both clinical and subclinical vertebral fractures are, in fact, independently associated with increased mortality,4, 14, 15 particularly in the period immediately following the event.16 The economic impact of osteoporosis and its related fractures is substantial.1719 In 1995, the annual direct medical cost for the inpatient care of vertebral fractures was estimated to be $575 million.19

Evidence‐Based Approach to the Hospitalized Patient

Evaluation

Vertebral fractures most commonly occur between T7 and L4. Acute vertebral fracture pain is typically sudden in onset and located in the mid to lower back. The pain may occur while performing an ordinary task such as lifting an object or bending over, although in many cases there is no preceding trauma. Physical activity exacerbates the pain and patients' movements may be limited due to pain. Spinal tenderness is usually present. A history of weight loss, prior malignancy, or fever should serve as red flags to the hospitalist and prompt evaluation for underlying malignancy or infection.

For a suspected fracture, frontal and lateral radiographs of the thoracolumbar spine are the initial imaging of choice. Magnetic resonance imaging (MRI) is useful when infection, malignancy, or spinal cord compression is suspected. The presence of neurological deficits should always prompt imaging with MRI or computed tomography (CT).

Patients with vertebral fractures should be evaluated with bone density testing, as osteoporosis is usually the underlying etiology. Because a number of medical conditions commonly contribute to bone loss (Table 1), laboratory testing is indicated for most patients. Although debate exists as to the optimal testing strategy,20 the National Osteoporosis Foundation guidelines recommend the evaluation of blood count, chemistry, and thyroid‐stimulating hormone (TSH) in patients with osteoporosis.21 Because vitamin D deficiency is common in patients who sustain osteoporotic fractures, 25‐hydroxyvitamin D levels should be checked in most patients.2225 Depending on the clinical scenario, additional testing may include the following: serum testosterone, serum intact parathyroid hormone (PTH), 24‐hour calcium excretion, serum protein electrophoresis, urine protein electrophoresis, erythrocyte sedimentation rate, and celiac sprue antibodies.26

Secondary Causes of Low Bone Mineral Density
Endocrine disease or metabolic causes Hypogonadism
Cushing's syndrome
Hyperthyroidism
Anorexia nervosa
Hyperparathyroidism
Nutritional conditions Vitamin D deficiency
Calcium deficiency
Malabsorption
Drugs Glucocorticoids
Antiepileptic drugs
Excessive thyroid medication
Long‐term heparin or low molecular weight heparin therapy (eg, >1 month)
Other Multiple myeloma
Rheumatoid arthritis
Organ transplantation

Management

Short Term

Short‐term management goals include the relief of pain and recovery of mobility. Nonsteroidal antiinflammatory drugs (NSAIDs) and low‐dose opioid medications should be used first in an effort to relieve pain. Beyond its potential effect on bone density, calcitonin (Miacalcin, Fortical) has long been used in acute vertebral fractures for analgesia. A systematic review of 5 randomized controlled trials concluded that calcitonin significantly reduced the pain from acute vertebral fractures.27 Calcitonin improved pain as early as 1 week into treatment and the benefit was persistent at 4 weeks. The analgesic mechanism of action for calcitonin is not entirely clear. Proposed mechanisms include increased ‐endorphin release, an antiinflammatory effect, and a direct effect on specific receptors in the central nervous system.27, 28

Pamidronate (Aredia) also has shown efficacy at reducing acute fracture pain. In a double‐blinded trial, Armigeat et al.29 evaluated pamidronate 30 mg daily for 3 days as compared to placebo in 32 patients. Pain scores were improved at 7 and 30 days with pamidronate. The mechanism of analgesia is unclear. Bisphosphonates are known to inhibit osteoclast activity, but may also work by blocking the effect of inflammatory cytokines.29

Early pain relief is critical in order to encourage physical activity. Bed rest should be avoided, as immobility may increase the risk for pressure ulcers, venous thromboembolism, and pneumonia.3033 Although bracing is frequently used in acute vertebral fracture, the modality has not been formally studied. Although also not well studied in the acute setting, physical therapy has been shown to reduce pain and improve functioning for patients with chronic pain from vertebral fracture,34 and is generally recommended.35

Percutaneous vertebral augmentation procedures include vertebroplasty and kyphoplasty. In vertebroplasty, polymethylmethacrylate cement is injected through a needle under fluoroscopic guidance into the collapsed vertebral body. With kyphoplasty, balloon tamps are used to elevate vertebral endplates prior to injection of cement (Figure 1).36 The proposed mechanism of action for both procedures is stabilization of the fracture by the hardened polymethylmethacrylate cement. These procedures are commonly performed by interventional radiologists without the need for general anesthesia; however, depending on the institution, they may be done by orthopedic surgeons, neurosurgeons, or anesthesiologists. The procedure can be performed as an outpatient, if indicated. Although the volume of these procedures has grown dramatically in recent years,37, 38 the quality of evidence supporting their use is relatively weak.3941 Only 1 randomized controlled trial has been published evaluating the potential benefit of vertebroplasty over conservative management.42 Voormolen et al.42 evaluated patients with vertebral fractures and pain refractive to 6 weeks of optimal medical therapy. Patients were treated with vertebroplasty or continuation of medical therapy. Vertebroplasty significantly improved pain initially, but not after 2 weeks. Like the study by Voormolen et al.,42 most studies evaluating percutaneous vertebral augmentation procedures have been conducted on patients with long‐term pain refractory to medical management. One notable exception is a nonrandomized trial published by Diamond et al.6 In that study, 55 patients were treated with vertebroplasty while 24 were treated conservatively. Pain at 24 hours was significantly improved in patients treated with vertebroplasty. At 6 weeks, however, there was no difference among the 2 groups.

Figure 1
Kyphoplasty. (A) In kyphoplasty, a cannula is placed into the collapsed vertebra, through which an inflatable bone tamp is inserted into the vertebral body. (B) The bone tamp is inflated, and (C) the cavity is filled with polymethylmethacrylate cement. (D) The hardened cement forms an internal cast. [Adapted from Mazanec et al.36 with permission]

The risk of short‐term complications from vertebral augmentation procedures is difficult to assess in light of the small sample sizes and methodological limitations of existing studies. Cement leakage occurs in 40% to 41% of patients treated with vertebroplasty, as compared with 8% to 9% with kyphoplasty.39, 41 Pulmonary emboli occur in 0.6% and 0.01% of patients treated with vertebroplasty and kyphoplasty, respectively, while neurologic complications occur in 0.6% and 0.03% of patients.41 Concern exists about whether percutaneous vertebral augmentation procedures might increase the risk for subsequent fractures,43, 44 as the incidence of new fractures appears to be elevated in the period immediately following the procedure and approximately two‐thirds of new fractures occur in vertebrae adjacent to the augmented vertebra.39, 41, 44 However, the 20% incidence of new vertebral fractures in the year following vertebral augmentation is similar to the fracture rate seen in patients not treated with osteoporosis therapy.44

Assessment of the impact of vertebral augmentation procedures on the cost of care is limited by the lack of high‐quality clinical studies.45, 46 Randomized controlled trials evaluating the benefit and risk of these procedures compared to conservative management are underway.4749 Pending further evidence, these procedures are best reserved for patients who fail to benefit from other measures to control pain and improve mobility.

Long Term

A comprehensive discussion of the long‐term management of osteoporosis is beyond the scope of this work. However, the inpatient setting presents an opportune time to initiate long‐term medical therapy. Studies show that the majority of patients who sustain osteoporotic fractures do not receive pharmacologic treatment for osteoporosis.5053 Hospitalists have the opportunity to start medications that can reduce the risk for subsequent fracture by nearly 50%.5458 A total calcium intake of 1200 to 1500 mg per day and vitamin D of 400 to 800 IU per day are recommended for all postmenopausal women. Patients who smoke should receive smoking cessation counseling and be considered for pharmacologic treatment for tobacco dependence. All patients should be assessed for fall risk, including a review of medications and assessment of alcohol intake.

Before considering pharmacologic treatment for osteoporosis, secondary causes of low bone mass must be excluded. Bisphosphonates are generally considered first‐line pharmacologic therapy for osteoporosis. Alendronate (Fosamax), risedronate (Actonel), and ibandronate (Boniva) have been shown in randomized trials to increase bone density and reduce the risk of osteoporotic fractures.55, 56, 59 Daily, weekly, and monthly preparations of bisphosphonates now exist. Pill‐induced esophagitis is a potential adverse effect of bisphosphonate therapy, but is extremely rare if proper precautions are taken. Patients should take oral bisphosphonates on an empty stomach, with a full glass of water, sitting upright, and have nothing to eat or drink for at least one half hour. If compliance with oral bisphosphonates is not possible, or esophageal abnormalities preclude oral bisphosphonate use, one may consider the use of intravenous ibandronate or zoledronic acid (Reclast). A 3‐year randomized controlled trial of yearly zoledronic acid improved bone density and reduced the incidence of osteoporotic fractures.60 Bisphosphonates are generally not recommended when creatinine clearance is less than 30 mL/minute. Other pharmacologic options for the treatment of osteoporosis include selective estrogen receptor modulators and anabolic agents. The reader is referred to an excellent review by Rosen61 for additional discussion of these therapies. The American College of Rheumatology clinical guidelines for the management of glucocorticoid induced osteoporosis are also worthy of review.62

Hospitalists are naturally suited to improve the quality of care for patients hospitalized with vertebral fractures. Most patients who currently sustain osteoporotic fractures do not receive appropriate evaluation and treatment. One study used an interdisciplinary team to identify, assess, and begin treatment for appropriate patients hospitalized with osteoporotic fractures.63 The intervention resulted in significantly more patients taking osteoporosis treatment medications 6 months after the incident fracture.

Conclusions

Acute vertebral fracture is a common clinical problem associated with significant morbidity and increased risk of mortality. Treatment of vertebral fracture should include analgesics and physical therapy. Percutaneous augmentation procedures may be considered in patients who fail optimal medical therapy. Because most vertebral fractures are due to osteoporosis and the healthcare system currently fails to appropriately assess and treat most patients who have sustained osteoporotic fractures, hospitalists are in an optimal position to initiate long‐term preventative treatment for these patients.

References
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  2. Melton LJ,Lane AW,Cooper C,Eastell R,O'Fallon WM,Riggs BL.Prevalence and incidence of vertebral deformities.Osteoporos Int.1993;3(3):113119.
  3. Cooper C,O'Neill T,Silman A.The epidemiology of vertebral fractures. European Vertebral Osteoporosis Study Group.Bone.1993;14(suppl 1):S89S97.
  4. Kado DM,Browner WS,Palermo L,Nevitt MC,Genant HK,Cummings SR.Vertebral fractures and mortality in older women: a prospective study. Study of Osteoporotic Fractures Research Group.Arch Intern Med.1999;159(11):12151220.
  5. Ross PD,Davis JW,Epstein RS,Wasnich RD.Pre‐existing fractures and bone mass predict vertebral fracture incidence in women.Ann Intern Med.1991;114(11):919923.
  6. Lindsay R,Silverman SL,Cooper C, et al.Risk of new vertebral fracture in the year following a fracture.JAMA.2001;285(3):320323.
  7. Gehlbach SH,Bigelow C,Heimisdottir M,May S,Walker M,Kirkwood JR.Recognition of vertebral fracture in a clinical setting.Osteoporos Int.2000;11(7):577582.
  8. Diamond TH,Champion B,Clark WA.Management of acute osteoporotic vertebral fractures: a nonrandomized trial comparing percutaneous vertebroplasty with conservative therapy.Am J Med.2003;114(4):257265.
  9. Silverman SL.The clinical consequences of vertebral compression fracture.Bone.1992;13(suppl 2):S27S31.
  10. Hall SE,Criddle RA,Comito TL,Prince RL.A case‐control study of quality of life and functional impairment in women with long‐standing vertebral osteoporotic fracture.Osteoporos Int.1999;9(6):508515.
  11. Salaffi F,Cimmino MA,Malavolta N, et al.The burden of prevalent fractures on health‐related quality of life in postmenopausal women with osteoporosis: the IMOF study.J Rheumatol.2007;34(7):15511560.
  12. Silverman SL,Minshall ME,Shen W,Harper KD,Xie S.The relationship of health‐related quality of life to prevalent and incident vertebral fractures in postmenopausal women with osteoporosis: results from the Multiple Outcomes of Raloxifene Evaluation Study.Arthritis Rheum2001;44(11):26112619.
  13. Nevitt MC,Ettinger B,Black DM, et al.The association of radiographically detected vertebral fractures with back pain and function: a prospective study.Ann Int Med.1998;128:793800.
  14. Center JR,Nguyen TV,Schneider D,Sambrook PN,Eisman JA.Mortality after all major types of osteoporotic fracture in men and women: an observational study.Lancet. 131999;353(9156):878882.
  15. Cooper C,Atkinson EJ,Jacobsen SJ,O'Fallon WM,Melton LJ.Population‐based study of survival after osteoporotic fractures.Am J Epidemiol.1993;137(9):10011005.
  16. Kanis JA,Oden A,Johnell O,De Laet C,Jonsson B.Excess mortality after hospitalisation for vertebral fracture.Osteoporos Int.2004;15(2):108112.
  17. Dolan P,Torgerson DJ.The cost of treating osteoporotic fractures in the United Kingdom female population.Osteoporos Int.1998;8(6):611617.
  18. Gabriel SE,Tosteson AN,Leibson CL, et al.Direct medical costs attributable to osteoporotic fractures.Osteoporos Int.2002;13(4):323330.
  19. Ray NF,Chan JK,Thamer M,Melton LJ.Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: report from the National Osteoporosis Foundation.J Bone Miner Res.1997;12(1):2435.
  20. Crandall C.Laboratory workup for osteoporosis. Which tests are most cost‐effective?Postgrad Med.2003;114(3):3538,4134.
  21. National Osteoporosis Foundation. Clinician's Guide to Prevention and Treatment of Osteoporosis. Available at: http://www.nof.org/professionals/Clinicians_Guide.htm. Accessed February2009.
  22. Holick M,Siris E,Binkley N, et al.Prevalence of vitamin D inadequacy among postmenopausal North American women receiving osteoporosis therapy.J Clin Endocr Metab.2005;90:32153224.
  23. Simonelli CS,Weiss TW,Morancey J,Swanson L,Chen Y.Prevalence of vitamin D inadequacy in a minimal trauma fracture population.Curr Med Res Opin.2005;21:10691074.
  24. LeBoff MS,Kohlmeier L,Hurwitz S,Franklin J,Wright J,Glowacki J.Occult vitamin D deficiency in postmenopausal US women with acute hip fracture.JAMA.1999;281:15051511.
  25. Edwards BJ,Langman CB,Bunta AD,Vicuna M,Favus M.Secondary contributors for bone loss in osteoporotic hip fractures.Osteoporos Int.2008;19(7):991999.
  26. Kleerekoper M.Evaluation of the patient with osteoporosis or at risk for osteoporosis. In: Marcus R, Feldman D, Kelsey J, eds.Osteoporosis. Vol.2.San Diego:Academic Press;2001:403408.
  27. Knopp JA,Diner BM,Blitz M,Lyritis GP,Rowe BH.Calcitonin for treating acute pain of osteoporotic vertebral compression fractures: a systematic review of randomized, controlled trials.Osteoporos Int.2005;16(10):12811290.
  28. Azria M.Possible mechanisms of the analgesic action of calcitonin.Bone.2002;30(5 suppl):80S83S.
  29. Armingeat T,Brondino R,Pham T,Legre V,Lafforgue P.Intravenous pamidronate for pain relief in recent osteoporotic vertebral compression fracture: a randomized double‐blind controlled study.Osteoporos Int.2006;17(11):16591665.
  30. Allman RM,Goode PS,Patrick MM,Burst N,Bartolucci AA.Pressure ulcer risk factors among hospitalized patients with activity limitation.JAMA.1995;273(11):865870.
  31. Anderson FA,Spencer FA.Risk factors for venous thromboembolism.Circulation. 172003;107(23 suppl 1):I9I16.
  32. Beck‐Sague C,Banerjee S,Jarvis WR.Infectious diseases and mortality among US nursing home residents.Am J Public Health.1993;83(12):17391742.
  33. Loeb M,McGeer A,McArthur M,Walter S,Simor AE.Risk factors for pneumonia and other lower respiratory tract infections in elderly residents of long‐term care facilities.Arch Intern Med. 271999;159(17):20582064.
  34. Malmros B,Mortensen L,Jensen MB,Charles P.Positive effects of physiotherapy on chronic pain and performance in osteoporosis.Osteoporos Int.1998;8(3):215221.
  35. Bonner FJ,Sinaki M,Grabois M, et al.Health professional's guide to rehabilitation of the patient with osteoporosis.Osteoporos Int.2003;14(suppl 2):S1S22.
  36. Mazanec DJ,Podichetty VK,Mompoint A,Potnis A.Vertebral compression fractures: manage aggressively to prevent sequelae.Cleve Clin J Med.2003;70(2):147156. Reprinted with permission. Copyright (c) 2003 Cleveland Clinic Foundation. All rights reserved.
  37. Morrison WB,Parker L,Frangos AJ,Carrino JA.Vertebroplasty in the United States: guidance method and provider distribution, 2001–2003.Radiology.2007;243(1):166170.
  38. Gray DT,Hollingworth W,Onwudiwe N,Deyo RA,Jarvik JG.Thoracic and lumbar vertebroplasties performed in US Medicare enrollees, 2001–2005.JAMA.2007;298(15):17601762.
  39. Taylor RS,Taylor RJ,Fritzell P.Balloon kyphoplasty and vertebroplasty for vertebral compression fractures: a comparative systematic review of efficacy and safety.Spine.2006;31(23):27472755.
  40. Bouza C,Lopez T,Magro A,Navalpotro L,Amate JM.Efficacy and safety of balloon kyphoplasty in the treatment of vertebral compression fractures: a systematic review.Eur Spine J.2006;15(7):10501067.
  41. Hulme PA,Krebs J,Ferguson SJ,Berlemann U.Vertebroplasty and kyphoplasty: a systematic review of 69 clinical studies.Spine.2006;31(17):19832001.
  42. Voormolen MH,Mali WP,Lohle PN, et al.Percutaneous vertebroplasty compared with optimal pain medication treatment: short‐term clinical outcome of patients with subacute or chronic painful osteoporotic vertebral compression fractures. The VERTOS study.AJNR Am J Neuroradiol.2007;28(3):555560.
  43. Lavelle WF,Cheney R.Recurrent fracture after vertebral kyphoplasty.Spine J.2006;6(5):488493.
  44. Trout AT,Kallmes DF.Does vertebroplasty cause incident vertebral fractures? A review of available data.AJNR Am J Neuroradiol.2006;27(7):13971403.
  45. Centers for Medicare and Medicaid Services. Agency for Healthcare Research and Quality (AHRQ). Technology Assessment. Percutaneous Kyphoplasty for Vertebral Fractures Caused by Osteoporosis and Malignancy, 2005. Available at: http://www.cms.hhs.gov/mcd/viewtechassess.asp?from2=viewtechassess.asp13(5):550555.
  46. Kallmes DF.Randomized vertebroplasty trials: current status and challenges.Acad Radiol.2006;13(5):546549.
  47. Klazen CA,Verhaar HJ,Lampmann LE, et al.VERTOS II: percutaneous vertebroplasty versus conservative therapy in patients with painful osteoporotic vertebral compression fractures; rationale, objectives and design of a multicenter randomized controlled trial.Trials.2007;8(1):33.
  48. Buchbinder R,Osborne RH.Vertebroplasty: a promising but as yet unproven intervention for painful osteoporotic spinal fractures.Med J Aust.2006;185(7):351352.
  49. Andrade SE,Majumdar SR,Chan KA, et al.Low frequency of treatment of osteoporosis among postmenopausal women following a fracture.Arch Intern Med.2003;163(17):20522057.
  50. Kamel HK,Hussain MS,Tariq S,Perry HM,Morley JE.Failure to diagnose and treat osteoporosis in elderly patients hospitalized with hip fracture.Am J Med.2000;109(4):326328.
  51. Smith MD,Ross W,Ahern MJ.Missing a therapeutic window of opportunity: an audit of patients attending a tertiary teaching hospital with potentially osteoporotic hip and wrist fractures.J Rheumatol.2001;28(11):25042508.
  52. Solomon DH,Finkelstein JS,Katz JN,Mogun H,Avorn J.Underuse of osteoporosis medications in elderly patients with fractures.Am J Med.2003;115(5):398400.
  53. Black DM,Cummings SR,Karpf DB, et al.Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures. Fracture Intervention Trial Research Group.Lancet.1996;348(9041):15351541.
  54. Cranney A,Guyatt G,Griffith L,Wells G,Tugwell P,Rosen C.Meta‐analyses of therapies for postmenopausal osteoporosis. IX: Summary of meta‐analyses of therapies for postmenopausal osteoporosis.Endocr Rev.2002;23(4):570578.
  55. Guyatt GH,Cranney A,Griffith L, et al.Summary of meta‐analyses of therapies for postmenopausal osteoporosis and the relationship between bone density and fractures.Endocrinol Metab Clin North Am.2002;31(3):659679, xii.
  56. Harris ST,Watts NB,Genant HK, et al.Effects of risedronate treatment on vertebral and nonvertebral fractures in women with postmenopausal osteoporosis: a randomized controlled trial. Vertebral Efficacy With Risedronate Therapy (VERT) Study Group.JAMA.1999;282(14):13441352.
  57. McClung MR,Geusens P,Miller PD, et al.Effect of risedronate on the risk of hip fracture in elderly women. Hip Intervention Program Study Group.N Engl J Med.2001;344(5):333340.
  58. Chesnut IC,Skag A,Christiansen C, et al.Effects of oral ibandronate administered daily or intermittently on fracture risk in postmenopausal osteoporosis.J Bone Miner Res.2004;19(8):12411249.
  59. Black DM,Delmas PD,Eastell R, et al.Once‐yearly zoledronic acid for treatment of postmenopausal osteoporosis.N Engl J Med. 32007;356(18):18091822.
  60. Rosen CJ.Clinical practice. Postmenopausal osteoporosis.N Engl J Med.2005;353(6):595603.
  61. American College of Rheumatology Ad Hoc Committee on Glucocorticoid‐Induced Osteoporosis.Recommendations for the prevention and treatment of glucocorticoid‐induced osteoporosis: 2001 update. [Review].Arthritis Rheum.2001;44(7):14961503.
  62. Edwards BJ,Bunta AD,Madison LD, et al.An osteoporosis and fracture intervention program increases the diagnosis and treatment for osteoporosis for patients with minimal trauma fractures.Jt Comm J Qual Patient Saf.2005;31(5):267274.
Article PDF
Issue
Journal of Hospital Medicine - 4(7)
Page Number
E20-E24
Legacy Keywords
fracture, kyphoplasty, osteoporosis, osteoporosis fracture, spine fracture, vertebral compression fracture, vertebral fracture, vertebroplasty
Sections
Article PDF
Article PDF

An 89‐year‐old female presents to the Emergency Department with lower back pain for the past 5 days. The patient has a past medical history of polymyalgia rheumatica and hypothyroidism. Her medications include prednisone 10 mg daily and levothyroxine 50 g daily. Aside from tenderness over the third lumbar vertebra, her physical exam is unremarkable. An x‐ray shows a fracture of the third lumbar vertebra. Basic laboratory studies, including calcium and creatinine are normal.

The Clinical Problem and Impact

Vertebral fractures (often termed vertebral compression fractures) affect approximately 25% of all postmenopausal women.1, 2 Only one‐third of vertebral fractures are brought to medical attention.3, 4 In the remaining two‐thirds, patients are either asymptomatic or do not seek medical attention. The lifetime risk of a clinical vertebral fracture is approximately 16% and 5% in white women and men, respectively.1 The risk of vertebral fracture increases with age, lower bone mineral density (BMD), and prior vertebral fracture.1, 5 Women with a preexisting vertebral fracture have a 5‐fold increased risk for a new vertebral fracture relative to those without a history of vertebral fracture.5, 6 Approximately 20% of women who sustain a vertebral fracture will have a new vertebral fracture in the subsequent year.5, 6 Vertebral fractures are frequently overlooked on chest x‐rays and hence there is a need for increased awareness and improved recognition of radiographically‐demonstrated fractures.7 Hospitalists must appreciate that a vertebral fracture is often the first clue to underlying osteoporosis. At least 90% of vertebral and hip fractures are attributable to osteoporosis.5

Typically, the pain related to an acute vertebral fracture improves over 4 to 6 weeks.8, 9 However, pain can persist, resulting in functional impairment, and a decline in the quality of life.1013 Vertebral fractures may lead to kyphosis and reduced lung function. This, in turn, may increase the risk for pneumonia, the most common cause of death in patients with osteoporosis.4 Both clinical and subclinical vertebral fractures are, in fact, independently associated with increased mortality,4, 14, 15 particularly in the period immediately following the event.16 The economic impact of osteoporosis and its related fractures is substantial.1719 In 1995, the annual direct medical cost for the inpatient care of vertebral fractures was estimated to be $575 million.19

Evidence‐Based Approach to the Hospitalized Patient

Evaluation

Vertebral fractures most commonly occur between T7 and L4. Acute vertebral fracture pain is typically sudden in onset and located in the mid to lower back. The pain may occur while performing an ordinary task such as lifting an object or bending over, although in many cases there is no preceding trauma. Physical activity exacerbates the pain and patients' movements may be limited due to pain. Spinal tenderness is usually present. A history of weight loss, prior malignancy, or fever should serve as red flags to the hospitalist and prompt evaluation for underlying malignancy or infection.

For a suspected fracture, frontal and lateral radiographs of the thoracolumbar spine are the initial imaging of choice. Magnetic resonance imaging (MRI) is useful when infection, malignancy, or spinal cord compression is suspected. The presence of neurological deficits should always prompt imaging with MRI or computed tomography (CT).

Patients with vertebral fractures should be evaluated with bone density testing, as osteoporosis is usually the underlying etiology. Because a number of medical conditions commonly contribute to bone loss (Table 1), laboratory testing is indicated for most patients. Although debate exists as to the optimal testing strategy,20 the National Osteoporosis Foundation guidelines recommend the evaluation of blood count, chemistry, and thyroid‐stimulating hormone (TSH) in patients with osteoporosis.21 Because vitamin D deficiency is common in patients who sustain osteoporotic fractures, 25‐hydroxyvitamin D levels should be checked in most patients.2225 Depending on the clinical scenario, additional testing may include the following: serum testosterone, serum intact parathyroid hormone (PTH), 24‐hour calcium excretion, serum protein electrophoresis, urine protein electrophoresis, erythrocyte sedimentation rate, and celiac sprue antibodies.26

Secondary Causes of Low Bone Mineral Density
Endocrine disease or metabolic causes Hypogonadism
Cushing's syndrome
Hyperthyroidism
Anorexia nervosa
Hyperparathyroidism
Nutritional conditions Vitamin D deficiency
Calcium deficiency
Malabsorption
Drugs Glucocorticoids
Antiepileptic drugs
Excessive thyroid medication
Long‐term heparin or low molecular weight heparin therapy (eg, >1 month)
Other Multiple myeloma
Rheumatoid arthritis
Organ transplantation

Management

Short Term

Short‐term management goals include the relief of pain and recovery of mobility. Nonsteroidal antiinflammatory drugs (NSAIDs) and low‐dose opioid medications should be used first in an effort to relieve pain. Beyond its potential effect on bone density, calcitonin (Miacalcin, Fortical) has long been used in acute vertebral fractures for analgesia. A systematic review of 5 randomized controlled trials concluded that calcitonin significantly reduced the pain from acute vertebral fractures.27 Calcitonin improved pain as early as 1 week into treatment and the benefit was persistent at 4 weeks. The analgesic mechanism of action for calcitonin is not entirely clear. Proposed mechanisms include increased ‐endorphin release, an antiinflammatory effect, and a direct effect on specific receptors in the central nervous system.27, 28

Pamidronate (Aredia) also has shown efficacy at reducing acute fracture pain. In a double‐blinded trial, Armigeat et al.29 evaluated pamidronate 30 mg daily for 3 days as compared to placebo in 32 patients. Pain scores were improved at 7 and 30 days with pamidronate. The mechanism of analgesia is unclear. Bisphosphonates are known to inhibit osteoclast activity, but may also work by blocking the effect of inflammatory cytokines.29

Early pain relief is critical in order to encourage physical activity. Bed rest should be avoided, as immobility may increase the risk for pressure ulcers, venous thromboembolism, and pneumonia.3033 Although bracing is frequently used in acute vertebral fracture, the modality has not been formally studied. Although also not well studied in the acute setting, physical therapy has been shown to reduce pain and improve functioning for patients with chronic pain from vertebral fracture,34 and is generally recommended.35

Percutaneous vertebral augmentation procedures include vertebroplasty and kyphoplasty. In vertebroplasty, polymethylmethacrylate cement is injected through a needle under fluoroscopic guidance into the collapsed vertebral body. With kyphoplasty, balloon tamps are used to elevate vertebral endplates prior to injection of cement (Figure 1).36 The proposed mechanism of action for both procedures is stabilization of the fracture by the hardened polymethylmethacrylate cement. These procedures are commonly performed by interventional radiologists without the need for general anesthesia; however, depending on the institution, they may be done by orthopedic surgeons, neurosurgeons, or anesthesiologists. The procedure can be performed as an outpatient, if indicated. Although the volume of these procedures has grown dramatically in recent years,37, 38 the quality of evidence supporting their use is relatively weak.3941 Only 1 randomized controlled trial has been published evaluating the potential benefit of vertebroplasty over conservative management.42 Voormolen et al.42 evaluated patients with vertebral fractures and pain refractive to 6 weeks of optimal medical therapy. Patients were treated with vertebroplasty or continuation of medical therapy. Vertebroplasty significantly improved pain initially, but not after 2 weeks. Like the study by Voormolen et al.,42 most studies evaluating percutaneous vertebral augmentation procedures have been conducted on patients with long‐term pain refractory to medical management. One notable exception is a nonrandomized trial published by Diamond et al.6 In that study, 55 patients were treated with vertebroplasty while 24 were treated conservatively. Pain at 24 hours was significantly improved in patients treated with vertebroplasty. At 6 weeks, however, there was no difference among the 2 groups.

Figure 1
Kyphoplasty. (A) In kyphoplasty, a cannula is placed into the collapsed vertebra, through which an inflatable bone tamp is inserted into the vertebral body. (B) The bone tamp is inflated, and (C) the cavity is filled with polymethylmethacrylate cement. (D) The hardened cement forms an internal cast. [Adapted from Mazanec et al.36 with permission]

The risk of short‐term complications from vertebral augmentation procedures is difficult to assess in light of the small sample sizes and methodological limitations of existing studies. Cement leakage occurs in 40% to 41% of patients treated with vertebroplasty, as compared with 8% to 9% with kyphoplasty.39, 41 Pulmonary emboli occur in 0.6% and 0.01% of patients treated with vertebroplasty and kyphoplasty, respectively, while neurologic complications occur in 0.6% and 0.03% of patients.41 Concern exists about whether percutaneous vertebral augmentation procedures might increase the risk for subsequent fractures,43, 44 as the incidence of new fractures appears to be elevated in the period immediately following the procedure and approximately two‐thirds of new fractures occur in vertebrae adjacent to the augmented vertebra.39, 41, 44 However, the 20% incidence of new vertebral fractures in the year following vertebral augmentation is similar to the fracture rate seen in patients not treated with osteoporosis therapy.44

Assessment of the impact of vertebral augmentation procedures on the cost of care is limited by the lack of high‐quality clinical studies.45, 46 Randomized controlled trials evaluating the benefit and risk of these procedures compared to conservative management are underway.4749 Pending further evidence, these procedures are best reserved for patients who fail to benefit from other measures to control pain and improve mobility.

Long Term

A comprehensive discussion of the long‐term management of osteoporosis is beyond the scope of this work. However, the inpatient setting presents an opportune time to initiate long‐term medical therapy. Studies show that the majority of patients who sustain osteoporotic fractures do not receive pharmacologic treatment for osteoporosis.5053 Hospitalists have the opportunity to start medications that can reduce the risk for subsequent fracture by nearly 50%.5458 A total calcium intake of 1200 to 1500 mg per day and vitamin D of 400 to 800 IU per day are recommended for all postmenopausal women. Patients who smoke should receive smoking cessation counseling and be considered for pharmacologic treatment for tobacco dependence. All patients should be assessed for fall risk, including a review of medications and assessment of alcohol intake.

Before considering pharmacologic treatment for osteoporosis, secondary causes of low bone mass must be excluded. Bisphosphonates are generally considered first‐line pharmacologic therapy for osteoporosis. Alendronate (Fosamax), risedronate (Actonel), and ibandronate (Boniva) have been shown in randomized trials to increase bone density and reduce the risk of osteoporotic fractures.55, 56, 59 Daily, weekly, and monthly preparations of bisphosphonates now exist. Pill‐induced esophagitis is a potential adverse effect of bisphosphonate therapy, but is extremely rare if proper precautions are taken. Patients should take oral bisphosphonates on an empty stomach, with a full glass of water, sitting upright, and have nothing to eat or drink for at least one half hour. If compliance with oral bisphosphonates is not possible, or esophageal abnormalities preclude oral bisphosphonate use, one may consider the use of intravenous ibandronate or zoledronic acid (Reclast). A 3‐year randomized controlled trial of yearly zoledronic acid improved bone density and reduced the incidence of osteoporotic fractures.60 Bisphosphonates are generally not recommended when creatinine clearance is less than 30 mL/minute. Other pharmacologic options for the treatment of osteoporosis include selective estrogen receptor modulators and anabolic agents. The reader is referred to an excellent review by Rosen61 for additional discussion of these therapies. The American College of Rheumatology clinical guidelines for the management of glucocorticoid induced osteoporosis are also worthy of review.62

Hospitalists are naturally suited to improve the quality of care for patients hospitalized with vertebral fractures. Most patients who currently sustain osteoporotic fractures do not receive appropriate evaluation and treatment. One study used an interdisciplinary team to identify, assess, and begin treatment for appropriate patients hospitalized with osteoporotic fractures.63 The intervention resulted in significantly more patients taking osteoporosis treatment medications 6 months after the incident fracture.

Conclusions

Acute vertebral fracture is a common clinical problem associated with significant morbidity and increased risk of mortality. Treatment of vertebral fracture should include analgesics and physical therapy. Percutaneous augmentation procedures may be considered in patients who fail optimal medical therapy. Because most vertebral fractures are due to osteoporosis and the healthcare system currently fails to appropriately assess and treat most patients who have sustained osteoporotic fractures, hospitalists are in an optimal position to initiate long‐term preventative treatment for these patients.

An 89‐year‐old female presents to the Emergency Department with lower back pain for the past 5 days. The patient has a past medical history of polymyalgia rheumatica and hypothyroidism. Her medications include prednisone 10 mg daily and levothyroxine 50 g daily. Aside from tenderness over the third lumbar vertebra, her physical exam is unremarkable. An x‐ray shows a fracture of the third lumbar vertebra. Basic laboratory studies, including calcium and creatinine are normal.

The Clinical Problem and Impact

Vertebral fractures (often termed vertebral compression fractures) affect approximately 25% of all postmenopausal women.1, 2 Only one‐third of vertebral fractures are brought to medical attention.3, 4 In the remaining two‐thirds, patients are either asymptomatic or do not seek medical attention. The lifetime risk of a clinical vertebral fracture is approximately 16% and 5% in white women and men, respectively.1 The risk of vertebral fracture increases with age, lower bone mineral density (BMD), and prior vertebral fracture.1, 5 Women with a preexisting vertebral fracture have a 5‐fold increased risk for a new vertebral fracture relative to those without a history of vertebral fracture.5, 6 Approximately 20% of women who sustain a vertebral fracture will have a new vertebral fracture in the subsequent year.5, 6 Vertebral fractures are frequently overlooked on chest x‐rays and hence there is a need for increased awareness and improved recognition of radiographically‐demonstrated fractures.7 Hospitalists must appreciate that a vertebral fracture is often the first clue to underlying osteoporosis. At least 90% of vertebral and hip fractures are attributable to osteoporosis.5

Typically, the pain related to an acute vertebral fracture improves over 4 to 6 weeks.8, 9 However, pain can persist, resulting in functional impairment, and a decline in the quality of life.1013 Vertebral fractures may lead to kyphosis and reduced lung function. This, in turn, may increase the risk for pneumonia, the most common cause of death in patients with osteoporosis.4 Both clinical and subclinical vertebral fractures are, in fact, independently associated with increased mortality,4, 14, 15 particularly in the period immediately following the event.16 The economic impact of osteoporosis and its related fractures is substantial.1719 In 1995, the annual direct medical cost for the inpatient care of vertebral fractures was estimated to be $575 million.19

Evidence‐Based Approach to the Hospitalized Patient

Evaluation

Vertebral fractures most commonly occur between T7 and L4. Acute vertebral fracture pain is typically sudden in onset and located in the mid to lower back. The pain may occur while performing an ordinary task such as lifting an object or bending over, although in many cases there is no preceding trauma. Physical activity exacerbates the pain and patients' movements may be limited due to pain. Spinal tenderness is usually present. A history of weight loss, prior malignancy, or fever should serve as red flags to the hospitalist and prompt evaluation for underlying malignancy or infection.

For a suspected fracture, frontal and lateral radiographs of the thoracolumbar spine are the initial imaging of choice. Magnetic resonance imaging (MRI) is useful when infection, malignancy, or spinal cord compression is suspected. The presence of neurological deficits should always prompt imaging with MRI or computed tomography (CT).

Patients with vertebral fractures should be evaluated with bone density testing, as osteoporosis is usually the underlying etiology. Because a number of medical conditions commonly contribute to bone loss (Table 1), laboratory testing is indicated for most patients. Although debate exists as to the optimal testing strategy,20 the National Osteoporosis Foundation guidelines recommend the evaluation of blood count, chemistry, and thyroid‐stimulating hormone (TSH) in patients with osteoporosis.21 Because vitamin D deficiency is common in patients who sustain osteoporotic fractures, 25‐hydroxyvitamin D levels should be checked in most patients.2225 Depending on the clinical scenario, additional testing may include the following: serum testosterone, serum intact parathyroid hormone (PTH), 24‐hour calcium excretion, serum protein electrophoresis, urine protein electrophoresis, erythrocyte sedimentation rate, and celiac sprue antibodies.26

Secondary Causes of Low Bone Mineral Density
Endocrine disease or metabolic causes Hypogonadism
Cushing's syndrome
Hyperthyroidism
Anorexia nervosa
Hyperparathyroidism
Nutritional conditions Vitamin D deficiency
Calcium deficiency
Malabsorption
Drugs Glucocorticoids
Antiepileptic drugs
Excessive thyroid medication
Long‐term heparin or low molecular weight heparin therapy (eg, >1 month)
Other Multiple myeloma
Rheumatoid arthritis
Organ transplantation

Management

Short Term

Short‐term management goals include the relief of pain and recovery of mobility. Nonsteroidal antiinflammatory drugs (NSAIDs) and low‐dose opioid medications should be used first in an effort to relieve pain. Beyond its potential effect on bone density, calcitonin (Miacalcin, Fortical) has long been used in acute vertebral fractures for analgesia. A systematic review of 5 randomized controlled trials concluded that calcitonin significantly reduced the pain from acute vertebral fractures.27 Calcitonin improved pain as early as 1 week into treatment and the benefit was persistent at 4 weeks. The analgesic mechanism of action for calcitonin is not entirely clear. Proposed mechanisms include increased ‐endorphin release, an antiinflammatory effect, and a direct effect on specific receptors in the central nervous system.27, 28

Pamidronate (Aredia) also has shown efficacy at reducing acute fracture pain. In a double‐blinded trial, Armigeat et al.29 evaluated pamidronate 30 mg daily for 3 days as compared to placebo in 32 patients. Pain scores were improved at 7 and 30 days with pamidronate. The mechanism of analgesia is unclear. Bisphosphonates are known to inhibit osteoclast activity, but may also work by blocking the effect of inflammatory cytokines.29

Early pain relief is critical in order to encourage physical activity. Bed rest should be avoided, as immobility may increase the risk for pressure ulcers, venous thromboembolism, and pneumonia.3033 Although bracing is frequently used in acute vertebral fracture, the modality has not been formally studied. Although also not well studied in the acute setting, physical therapy has been shown to reduce pain and improve functioning for patients with chronic pain from vertebral fracture,34 and is generally recommended.35

Percutaneous vertebral augmentation procedures include vertebroplasty and kyphoplasty. In vertebroplasty, polymethylmethacrylate cement is injected through a needle under fluoroscopic guidance into the collapsed vertebral body. With kyphoplasty, balloon tamps are used to elevate vertebral endplates prior to injection of cement (Figure 1).36 The proposed mechanism of action for both procedures is stabilization of the fracture by the hardened polymethylmethacrylate cement. These procedures are commonly performed by interventional radiologists without the need for general anesthesia; however, depending on the institution, they may be done by orthopedic surgeons, neurosurgeons, or anesthesiologists. The procedure can be performed as an outpatient, if indicated. Although the volume of these procedures has grown dramatically in recent years,37, 38 the quality of evidence supporting their use is relatively weak.3941 Only 1 randomized controlled trial has been published evaluating the potential benefit of vertebroplasty over conservative management.42 Voormolen et al.42 evaluated patients with vertebral fractures and pain refractive to 6 weeks of optimal medical therapy. Patients were treated with vertebroplasty or continuation of medical therapy. Vertebroplasty significantly improved pain initially, but not after 2 weeks. Like the study by Voormolen et al.,42 most studies evaluating percutaneous vertebral augmentation procedures have been conducted on patients with long‐term pain refractory to medical management. One notable exception is a nonrandomized trial published by Diamond et al.6 In that study, 55 patients were treated with vertebroplasty while 24 were treated conservatively. Pain at 24 hours was significantly improved in patients treated with vertebroplasty. At 6 weeks, however, there was no difference among the 2 groups.

Figure 1
Kyphoplasty. (A) In kyphoplasty, a cannula is placed into the collapsed vertebra, through which an inflatable bone tamp is inserted into the vertebral body. (B) The bone tamp is inflated, and (C) the cavity is filled with polymethylmethacrylate cement. (D) The hardened cement forms an internal cast. [Adapted from Mazanec et al.36 with permission]

The risk of short‐term complications from vertebral augmentation procedures is difficult to assess in light of the small sample sizes and methodological limitations of existing studies. Cement leakage occurs in 40% to 41% of patients treated with vertebroplasty, as compared with 8% to 9% with kyphoplasty.39, 41 Pulmonary emboli occur in 0.6% and 0.01% of patients treated with vertebroplasty and kyphoplasty, respectively, while neurologic complications occur in 0.6% and 0.03% of patients.41 Concern exists about whether percutaneous vertebral augmentation procedures might increase the risk for subsequent fractures,43, 44 as the incidence of new fractures appears to be elevated in the period immediately following the procedure and approximately two‐thirds of new fractures occur in vertebrae adjacent to the augmented vertebra.39, 41, 44 However, the 20% incidence of new vertebral fractures in the year following vertebral augmentation is similar to the fracture rate seen in patients not treated with osteoporosis therapy.44

Assessment of the impact of vertebral augmentation procedures on the cost of care is limited by the lack of high‐quality clinical studies.45, 46 Randomized controlled trials evaluating the benefit and risk of these procedures compared to conservative management are underway.4749 Pending further evidence, these procedures are best reserved for patients who fail to benefit from other measures to control pain and improve mobility.

Long Term

A comprehensive discussion of the long‐term management of osteoporosis is beyond the scope of this work. However, the inpatient setting presents an opportune time to initiate long‐term medical therapy. Studies show that the majority of patients who sustain osteoporotic fractures do not receive pharmacologic treatment for osteoporosis.5053 Hospitalists have the opportunity to start medications that can reduce the risk for subsequent fracture by nearly 50%.5458 A total calcium intake of 1200 to 1500 mg per day and vitamin D of 400 to 800 IU per day are recommended for all postmenopausal women. Patients who smoke should receive smoking cessation counseling and be considered for pharmacologic treatment for tobacco dependence. All patients should be assessed for fall risk, including a review of medications and assessment of alcohol intake.

Before considering pharmacologic treatment for osteoporosis, secondary causes of low bone mass must be excluded. Bisphosphonates are generally considered first‐line pharmacologic therapy for osteoporosis. Alendronate (Fosamax), risedronate (Actonel), and ibandronate (Boniva) have been shown in randomized trials to increase bone density and reduce the risk of osteoporotic fractures.55, 56, 59 Daily, weekly, and monthly preparations of bisphosphonates now exist. Pill‐induced esophagitis is a potential adverse effect of bisphosphonate therapy, but is extremely rare if proper precautions are taken. Patients should take oral bisphosphonates on an empty stomach, with a full glass of water, sitting upright, and have nothing to eat or drink for at least one half hour. If compliance with oral bisphosphonates is not possible, or esophageal abnormalities preclude oral bisphosphonate use, one may consider the use of intravenous ibandronate or zoledronic acid (Reclast). A 3‐year randomized controlled trial of yearly zoledronic acid improved bone density and reduced the incidence of osteoporotic fractures.60 Bisphosphonates are generally not recommended when creatinine clearance is less than 30 mL/minute. Other pharmacologic options for the treatment of osteoporosis include selective estrogen receptor modulators and anabolic agents. The reader is referred to an excellent review by Rosen61 for additional discussion of these therapies. The American College of Rheumatology clinical guidelines for the management of glucocorticoid induced osteoporosis are also worthy of review.62

Hospitalists are naturally suited to improve the quality of care for patients hospitalized with vertebral fractures. Most patients who currently sustain osteoporotic fractures do not receive appropriate evaluation and treatment. One study used an interdisciplinary team to identify, assess, and begin treatment for appropriate patients hospitalized with osteoporotic fractures.63 The intervention resulted in significantly more patients taking osteoporosis treatment medications 6 months after the incident fracture.

Conclusions

Acute vertebral fracture is a common clinical problem associated with significant morbidity and increased risk of mortality. Treatment of vertebral fracture should include analgesics and physical therapy. Percutaneous augmentation procedures may be considered in patients who fail optimal medical therapy. Because most vertebral fractures are due to osteoporosis and the healthcare system currently fails to appropriately assess and treat most patients who have sustained osteoporotic fractures, hospitalists are in an optimal position to initiate long‐term preventative treatment for these patients.

References
  1. Melton LJ.Epidemiology of spinal osteoporosis.Spine.1997;22(24 suppl):2S11S.
  2. Melton LJ,Lane AW,Cooper C,Eastell R,O'Fallon WM,Riggs BL.Prevalence and incidence of vertebral deformities.Osteoporos Int.1993;3(3):113119.
  3. Cooper C,O'Neill T,Silman A.The epidemiology of vertebral fractures. European Vertebral Osteoporosis Study Group.Bone.1993;14(suppl 1):S89S97.
  4. Kado DM,Browner WS,Palermo L,Nevitt MC,Genant HK,Cummings SR.Vertebral fractures and mortality in older women: a prospective study. Study of Osteoporotic Fractures Research Group.Arch Intern Med.1999;159(11):12151220.
  5. Ross PD,Davis JW,Epstein RS,Wasnich RD.Pre‐existing fractures and bone mass predict vertebral fracture incidence in women.Ann Intern Med.1991;114(11):919923.
  6. Lindsay R,Silverman SL,Cooper C, et al.Risk of new vertebral fracture in the year following a fracture.JAMA.2001;285(3):320323.
  7. Gehlbach SH,Bigelow C,Heimisdottir M,May S,Walker M,Kirkwood JR.Recognition of vertebral fracture in a clinical setting.Osteoporos Int.2000;11(7):577582.
  8. Diamond TH,Champion B,Clark WA.Management of acute osteoporotic vertebral fractures: a nonrandomized trial comparing percutaneous vertebroplasty with conservative therapy.Am J Med.2003;114(4):257265.
  9. Silverman SL.The clinical consequences of vertebral compression fracture.Bone.1992;13(suppl 2):S27S31.
  10. Hall SE,Criddle RA,Comito TL,Prince RL.A case‐control study of quality of life and functional impairment in women with long‐standing vertebral osteoporotic fracture.Osteoporos Int.1999;9(6):508515.
  11. Salaffi F,Cimmino MA,Malavolta N, et al.The burden of prevalent fractures on health‐related quality of life in postmenopausal women with osteoporosis: the IMOF study.J Rheumatol.2007;34(7):15511560.
  12. Silverman SL,Minshall ME,Shen W,Harper KD,Xie S.The relationship of health‐related quality of life to prevalent and incident vertebral fractures in postmenopausal women with osteoporosis: results from the Multiple Outcomes of Raloxifene Evaluation Study.Arthritis Rheum2001;44(11):26112619.
  13. Nevitt MC,Ettinger B,Black DM, et al.The association of radiographically detected vertebral fractures with back pain and function: a prospective study.Ann Int Med.1998;128:793800.
  14. Center JR,Nguyen TV,Schneider D,Sambrook PN,Eisman JA.Mortality after all major types of osteoporotic fracture in men and women: an observational study.Lancet. 131999;353(9156):878882.
  15. Cooper C,Atkinson EJ,Jacobsen SJ,O'Fallon WM,Melton LJ.Population‐based study of survival after osteoporotic fractures.Am J Epidemiol.1993;137(9):10011005.
  16. Kanis JA,Oden A,Johnell O,De Laet C,Jonsson B.Excess mortality after hospitalisation for vertebral fracture.Osteoporos Int.2004;15(2):108112.
  17. Dolan P,Torgerson DJ.The cost of treating osteoporotic fractures in the United Kingdom female population.Osteoporos Int.1998;8(6):611617.
  18. Gabriel SE,Tosteson AN,Leibson CL, et al.Direct medical costs attributable to osteoporotic fractures.Osteoporos Int.2002;13(4):323330.
  19. Ray NF,Chan JK,Thamer M,Melton LJ.Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: report from the National Osteoporosis Foundation.J Bone Miner Res.1997;12(1):2435.
  20. Crandall C.Laboratory workup for osteoporosis. Which tests are most cost‐effective?Postgrad Med.2003;114(3):3538,4134.
  21. National Osteoporosis Foundation. Clinician's Guide to Prevention and Treatment of Osteoporosis. Available at: http://www.nof.org/professionals/Clinicians_Guide.htm. Accessed February2009.
  22. Holick M,Siris E,Binkley N, et al.Prevalence of vitamin D inadequacy among postmenopausal North American women receiving osteoporosis therapy.J Clin Endocr Metab.2005;90:32153224.
  23. Simonelli CS,Weiss TW,Morancey J,Swanson L,Chen Y.Prevalence of vitamin D inadequacy in a minimal trauma fracture population.Curr Med Res Opin.2005;21:10691074.
  24. LeBoff MS,Kohlmeier L,Hurwitz S,Franklin J,Wright J,Glowacki J.Occult vitamin D deficiency in postmenopausal US women with acute hip fracture.JAMA.1999;281:15051511.
  25. Edwards BJ,Langman CB,Bunta AD,Vicuna M,Favus M.Secondary contributors for bone loss in osteoporotic hip fractures.Osteoporos Int.2008;19(7):991999.
  26. Kleerekoper M.Evaluation of the patient with osteoporosis or at risk for osteoporosis. In: Marcus R, Feldman D, Kelsey J, eds.Osteoporosis. Vol.2.San Diego:Academic Press;2001:403408.
  27. Knopp JA,Diner BM,Blitz M,Lyritis GP,Rowe BH.Calcitonin for treating acute pain of osteoporotic vertebral compression fractures: a systematic review of randomized, controlled trials.Osteoporos Int.2005;16(10):12811290.
  28. Azria M.Possible mechanisms of the analgesic action of calcitonin.Bone.2002;30(5 suppl):80S83S.
  29. Armingeat T,Brondino R,Pham T,Legre V,Lafforgue P.Intravenous pamidronate for pain relief in recent osteoporotic vertebral compression fracture: a randomized double‐blind controlled study.Osteoporos Int.2006;17(11):16591665.
  30. Allman RM,Goode PS,Patrick MM,Burst N,Bartolucci AA.Pressure ulcer risk factors among hospitalized patients with activity limitation.JAMA.1995;273(11):865870.
  31. Anderson FA,Spencer FA.Risk factors for venous thromboembolism.Circulation. 172003;107(23 suppl 1):I9I16.
  32. Beck‐Sague C,Banerjee S,Jarvis WR.Infectious diseases and mortality among US nursing home residents.Am J Public Health.1993;83(12):17391742.
  33. Loeb M,McGeer A,McArthur M,Walter S,Simor AE.Risk factors for pneumonia and other lower respiratory tract infections in elderly residents of long‐term care facilities.Arch Intern Med. 271999;159(17):20582064.
  34. Malmros B,Mortensen L,Jensen MB,Charles P.Positive effects of physiotherapy on chronic pain and performance in osteoporosis.Osteoporos Int.1998;8(3):215221.
  35. Bonner FJ,Sinaki M,Grabois M, et al.Health professional's guide to rehabilitation of the patient with osteoporosis.Osteoporos Int.2003;14(suppl 2):S1S22.
  36. Mazanec DJ,Podichetty VK,Mompoint A,Potnis A.Vertebral compression fractures: manage aggressively to prevent sequelae.Cleve Clin J Med.2003;70(2):147156. Reprinted with permission. Copyright (c) 2003 Cleveland Clinic Foundation. All rights reserved.
  37. Morrison WB,Parker L,Frangos AJ,Carrino JA.Vertebroplasty in the United States: guidance method and provider distribution, 2001–2003.Radiology.2007;243(1):166170.
  38. Gray DT,Hollingworth W,Onwudiwe N,Deyo RA,Jarvik JG.Thoracic and lumbar vertebroplasties performed in US Medicare enrollees, 2001–2005.JAMA.2007;298(15):17601762.
  39. Taylor RS,Taylor RJ,Fritzell P.Balloon kyphoplasty and vertebroplasty for vertebral compression fractures: a comparative systematic review of efficacy and safety.Spine.2006;31(23):27472755.
  40. Bouza C,Lopez T,Magro A,Navalpotro L,Amate JM.Efficacy and safety of balloon kyphoplasty in the treatment of vertebral compression fractures: a systematic review.Eur Spine J.2006;15(7):10501067.
  41. Hulme PA,Krebs J,Ferguson SJ,Berlemann U.Vertebroplasty and kyphoplasty: a systematic review of 69 clinical studies.Spine.2006;31(17):19832001.
  42. Voormolen MH,Mali WP,Lohle PN, et al.Percutaneous vertebroplasty compared with optimal pain medication treatment: short‐term clinical outcome of patients with subacute or chronic painful osteoporotic vertebral compression fractures. The VERTOS study.AJNR Am J Neuroradiol.2007;28(3):555560.
  43. Lavelle WF,Cheney R.Recurrent fracture after vertebral kyphoplasty.Spine J.2006;6(5):488493.
  44. Trout AT,Kallmes DF.Does vertebroplasty cause incident vertebral fractures? A review of available data.AJNR Am J Neuroradiol.2006;27(7):13971403.
  45. Centers for Medicare and Medicaid Services. Agency for Healthcare Research and Quality (AHRQ). Technology Assessment. Percutaneous Kyphoplasty for Vertebral Fractures Caused by Osteoporosis and Malignancy, 2005. Available at: http://www.cms.hhs.gov/mcd/viewtechassess.asp?from2=viewtechassess.asp13(5):550555.
  46. Kallmes DF.Randomized vertebroplasty trials: current status and challenges.Acad Radiol.2006;13(5):546549.
  47. Klazen CA,Verhaar HJ,Lampmann LE, et al.VERTOS II: percutaneous vertebroplasty versus conservative therapy in patients with painful osteoporotic vertebral compression fractures; rationale, objectives and design of a multicenter randomized controlled trial.Trials.2007;8(1):33.
  48. Buchbinder R,Osborne RH.Vertebroplasty: a promising but as yet unproven intervention for painful osteoporotic spinal fractures.Med J Aust.2006;185(7):351352.
  49. Andrade SE,Majumdar SR,Chan KA, et al.Low frequency of treatment of osteoporosis among postmenopausal women following a fracture.Arch Intern Med.2003;163(17):20522057.
  50. Kamel HK,Hussain MS,Tariq S,Perry HM,Morley JE.Failure to diagnose and treat osteoporosis in elderly patients hospitalized with hip fracture.Am J Med.2000;109(4):326328.
  51. Smith MD,Ross W,Ahern MJ.Missing a therapeutic window of opportunity: an audit of patients attending a tertiary teaching hospital with potentially osteoporotic hip and wrist fractures.J Rheumatol.2001;28(11):25042508.
  52. Solomon DH,Finkelstein JS,Katz JN,Mogun H,Avorn J.Underuse of osteoporosis medications in elderly patients with fractures.Am J Med.2003;115(5):398400.
  53. Black DM,Cummings SR,Karpf DB, et al.Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures. Fracture Intervention Trial Research Group.Lancet.1996;348(9041):15351541.
  54. Cranney A,Guyatt G,Griffith L,Wells G,Tugwell P,Rosen C.Meta‐analyses of therapies for postmenopausal osteoporosis. IX: Summary of meta‐analyses of therapies for postmenopausal osteoporosis.Endocr Rev.2002;23(4):570578.
  55. Guyatt GH,Cranney A,Griffith L, et al.Summary of meta‐analyses of therapies for postmenopausal osteoporosis and the relationship between bone density and fractures.Endocrinol Metab Clin North Am.2002;31(3):659679, xii.
  56. Harris ST,Watts NB,Genant HK, et al.Effects of risedronate treatment on vertebral and nonvertebral fractures in women with postmenopausal osteoporosis: a randomized controlled trial. Vertebral Efficacy With Risedronate Therapy (VERT) Study Group.JAMA.1999;282(14):13441352.
  57. McClung MR,Geusens P,Miller PD, et al.Effect of risedronate on the risk of hip fracture in elderly women. Hip Intervention Program Study Group.N Engl J Med.2001;344(5):333340.
  58. Chesnut IC,Skag A,Christiansen C, et al.Effects of oral ibandronate administered daily or intermittently on fracture risk in postmenopausal osteoporosis.J Bone Miner Res.2004;19(8):12411249.
  59. Black DM,Delmas PD,Eastell R, et al.Once‐yearly zoledronic acid for treatment of postmenopausal osteoporosis.N Engl J Med. 32007;356(18):18091822.
  60. Rosen CJ.Clinical practice. Postmenopausal osteoporosis.N Engl J Med.2005;353(6):595603.
  61. American College of Rheumatology Ad Hoc Committee on Glucocorticoid‐Induced Osteoporosis.Recommendations for the prevention and treatment of glucocorticoid‐induced osteoporosis: 2001 update. [Review].Arthritis Rheum.2001;44(7):14961503.
  62. Edwards BJ,Bunta AD,Madison LD, et al.An osteoporosis and fracture intervention program increases the diagnosis and treatment for osteoporosis for patients with minimal trauma fractures.Jt Comm J Qual Patient Saf.2005;31(5):267274.
References
  1. Melton LJ.Epidemiology of spinal osteoporosis.Spine.1997;22(24 suppl):2S11S.
  2. Melton LJ,Lane AW,Cooper C,Eastell R,O'Fallon WM,Riggs BL.Prevalence and incidence of vertebral deformities.Osteoporos Int.1993;3(3):113119.
  3. Cooper C,O'Neill T,Silman A.The epidemiology of vertebral fractures. European Vertebral Osteoporosis Study Group.Bone.1993;14(suppl 1):S89S97.
  4. Kado DM,Browner WS,Palermo L,Nevitt MC,Genant HK,Cummings SR.Vertebral fractures and mortality in older women: a prospective study. Study of Osteoporotic Fractures Research Group.Arch Intern Med.1999;159(11):12151220.
  5. Ross PD,Davis JW,Epstein RS,Wasnich RD.Pre‐existing fractures and bone mass predict vertebral fracture incidence in women.Ann Intern Med.1991;114(11):919923.
  6. Lindsay R,Silverman SL,Cooper C, et al.Risk of new vertebral fracture in the year following a fracture.JAMA.2001;285(3):320323.
  7. Gehlbach SH,Bigelow C,Heimisdottir M,May S,Walker M,Kirkwood JR.Recognition of vertebral fracture in a clinical setting.Osteoporos Int.2000;11(7):577582.
  8. Diamond TH,Champion B,Clark WA.Management of acute osteoporotic vertebral fractures: a nonrandomized trial comparing percutaneous vertebroplasty with conservative therapy.Am J Med.2003;114(4):257265.
  9. Silverman SL.The clinical consequences of vertebral compression fracture.Bone.1992;13(suppl 2):S27S31.
  10. Hall SE,Criddle RA,Comito TL,Prince RL.A case‐control study of quality of life and functional impairment in women with long‐standing vertebral osteoporotic fracture.Osteoporos Int.1999;9(6):508515.
  11. Salaffi F,Cimmino MA,Malavolta N, et al.The burden of prevalent fractures on health‐related quality of life in postmenopausal women with osteoporosis: the IMOF study.J Rheumatol.2007;34(7):15511560.
  12. Silverman SL,Minshall ME,Shen W,Harper KD,Xie S.The relationship of health‐related quality of life to prevalent and incident vertebral fractures in postmenopausal women with osteoporosis: results from the Multiple Outcomes of Raloxifene Evaluation Study.Arthritis Rheum2001;44(11):26112619.
  13. Nevitt MC,Ettinger B,Black DM, et al.The association of radiographically detected vertebral fractures with back pain and function: a prospective study.Ann Int Med.1998;128:793800.
  14. Center JR,Nguyen TV,Schneider D,Sambrook PN,Eisman JA.Mortality after all major types of osteoporotic fracture in men and women: an observational study.Lancet. 131999;353(9156):878882.
  15. Cooper C,Atkinson EJ,Jacobsen SJ,O'Fallon WM,Melton LJ.Population‐based study of survival after osteoporotic fractures.Am J Epidemiol.1993;137(9):10011005.
  16. Kanis JA,Oden A,Johnell O,De Laet C,Jonsson B.Excess mortality after hospitalisation for vertebral fracture.Osteoporos Int.2004;15(2):108112.
  17. Dolan P,Torgerson DJ.The cost of treating osteoporotic fractures in the United Kingdom female population.Osteoporos Int.1998;8(6):611617.
  18. Gabriel SE,Tosteson AN,Leibson CL, et al.Direct medical costs attributable to osteoporotic fractures.Osteoporos Int.2002;13(4):323330.
  19. Ray NF,Chan JK,Thamer M,Melton LJ.Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: report from the National Osteoporosis Foundation.J Bone Miner Res.1997;12(1):2435.
  20. Crandall C.Laboratory workup for osteoporosis. Which tests are most cost‐effective?Postgrad Med.2003;114(3):3538,4134.
  21. National Osteoporosis Foundation. Clinician's Guide to Prevention and Treatment of Osteoporosis. Available at: http://www.nof.org/professionals/Clinicians_Guide.htm. Accessed February2009.
  22. Holick M,Siris E,Binkley N, et al.Prevalence of vitamin D inadequacy among postmenopausal North American women receiving osteoporosis therapy.J Clin Endocr Metab.2005;90:32153224.
  23. Simonelli CS,Weiss TW,Morancey J,Swanson L,Chen Y.Prevalence of vitamin D inadequacy in a minimal trauma fracture population.Curr Med Res Opin.2005;21:10691074.
  24. LeBoff MS,Kohlmeier L,Hurwitz S,Franklin J,Wright J,Glowacki J.Occult vitamin D deficiency in postmenopausal US women with acute hip fracture.JAMA.1999;281:15051511.
  25. Edwards BJ,Langman CB,Bunta AD,Vicuna M,Favus M.Secondary contributors for bone loss in osteoporotic hip fractures.Osteoporos Int.2008;19(7):991999.
  26. Kleerekoper M.Evaluation of the patient with osteoporosis or at risk for osteoporosis. In: Marcus R, Feldman D, Kelsey J, eds.Osteoporosis. Vol.2.San Diego:Academic Press;2001:403408.
  27. Knopp JA,Diner BM,Blitz M,Lyritis GP,Rowe BH.Calcitonin for treating acute pain of osteoporotic vertebral compression fractures: a systematic review of randomized, controlled trials.Osteoporos Int.2005;16(10):12811290.
  28. Azria M.Possible mechanisms of the analgesic action of calcitonin.Bone.2002;30(5 suppl):80S83S.
  29. Armingeat T,Brondino R,Pham T,Legre V,Lafforgue P.Intravenous pamidronate for pain relief in recent osteoporotic vertebral compression fracture: a randomized double‐blind controlled study.Osteoporos Int.2006;17(11):16591665.
  30. Allman RM,Goode PS,Patrick MM,Burst N,Bartolucci AA.Pressure ulcer risk factors among hospitalized patients with activity limitation.JAMA.1995;273(11):865870.
  31. Anderson FA,Spencer FA.Risk factors for venous thromboembolism.Circulation. 172003;107(23 suppl 1):I9I16.
  32. Beck‐Sague C,Banerjee S,Jarvis WR.Infectious diseases and mortality among US nursing home residents.Am J Public Health.1993;83(12):17391742.
  33. Loeb M,McGeer A,McArthur M,Walter S,Simor AE.Risk factors for pneumonia and other lower respiratory tract infections in elderly residents of long‐term care facilities.Arch Intern Med. 271999;159(17):20582064.
  34. Malmros B,Mortensen L,Jensen MB,Charles P.Positive effects of physiotherapy on chronic pain and performance in osteoporosis.Osteoporos Int.1998;8(3):215221.
  35. Bonner FJ,Sinaki M,Grabois M, et al.Health professional's guide to rehabilitation of the patient with osteoporosis.Osteoporos Int.2003;14(suppl 2):S1S22.
  36. Mazanec DJ,Podichetty VK,Mompoint A,Potnis A.Vertebral compression fractures: manage aggressively to prevent sequelae.Cleve Clin J Med.2003;70(2):147156. Reprinted with permission. Copyright (c) 2003 Cleveland Clinic Foundation. All rights reserved.
  37. Morrison WB,Parker L,Frangos AJ,Carrino JA.Vertebroplasty in the United States: guidance method and provider distribution, 2001–2003.Radiology.2007;243(1):166170.
  38. Gray DT,Hollingworth W,Onwudiwe N,Deyo RA,Jarvik JG.Thoracic and lumbar vertebroplasties performed in US Medicare enrollees, 2001–2005.JAMA.2007;298(15):17601762.
  39. Taylor RS,Taylor RJ,Fritzell P.Balloon kyphoplasty and vertebroplasty for vertebral compression fractures: a comparative systematic review of efficacy and safety.Spine.2006;31(23):27472755.
  40. Bouza C,Lopez T,Magro A,Navalpotro L,Amate JM.Efficacy and safety of balloon kyphoplasty in the treatment of vertebral compression fractures: a systematic review.Eur Spine J.2006;15(7):10501067.
  41. Hulme PA,Krebs J,Ferguson SJ,Berlemann U.Vertebroplasty and kyphoplasty: a systematic review of 69 clinical studies.Spine.2006;31(17):19832001.
  42. Voormolen MH,Mali WP,Lohle PN, et al.Percutaneous vertebroplasty compared with optimal pain medication treatment: short‐term clinical outcome of patients with subacute or chronic painful osteoporotic vertebral compression fractures. The VERTOS study.AJNR Am J Neuroradiol.2007;28(3):555560.
  43. Lavelle WF,Cheney R.Recurrent fracture after vertebral kyphoplasty.Spine J.2006;6(5):488493.
  44. Trout AT,Kallmes DF.Does vertebroplasty cause incident vertebral fractures? A review of available data.AJNR Am J Neuroradiol.2006;27(7):13971403.
  45. Centers for Medicare and Medicaid Services. Agency for Healthcare Research and Quality (AHRQ). Technology Assessment. Percutaneous Kyphoplasty for Vertebral Fractures Caused by Osteoporosis and Malignancy, 2005. Available at: http://www.cms.hhs.gov/mcd/viewtechassess.asp?from2=viewtechassess.asp13(5):550555.
  46. Kallmes DF.Randomized vertebroplasty trials: current status and challenges.Acad Radiol.2006;13(5):546549.
  47. Klazen CA,Verhaar HJ,Lampmann LE, et al.VERTOS II: percutaneous vertebroplasty versus conservative therapy in patients with painful osteoporotic vertebral compression fractures; rationale, objectives and design of a multicenter randomized controlled trial.Trials.2007;8(1):33.
  48. Buchbinder R,Osborne RH.Vertebroplasty: a promising but as yet unproven intervention for painful osteoporotic spinal fractures.Med J Aust.2006;185(7):351352.
  49. Andrade SE,Majumdar SR,Chan KA, et al.Low frequency of treatment of osteoporosis among postmenopausal women following a fracture.Arch Intern Med.2003;163(17):20522057.
  50. Kamel HK,Hussain MS,Tariq S,Perry HM,Morley JE.Failure to diagnose and treat osteoporosis in elderly patients hospitalized with hip fracture.Am J Med.2000;109(4):326328.
  51. Smith MD,Ross W,Ahern MJ.Missing a therapeutic window of opportunity: an audit of patients attending a tertiary teaching hospital with potentially osteoporotic hip and wrist fractures.J Rheumatol.2001;28(11):25042508.
  52. Solomon DH,Finkelstein JS,Katz JN,Mogun H,Avorn J.Underuse of osteoporosis medications in elderly patients with fractures.Am J Med.2003;115(5):398400.
  53. Black DM,Cummings SR,Karpf DB, et al.Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures. Fracture Intervention Trial Research Group.Lancet.1996;348(9041):15351541.
  54. Cranney A,Guyatt G,Griffith L,Wells G,Tugwell P,Rosen C.Meta‐analyses of therapies for postmenopausal osteoporosis. IX: Summary of meta‐analyses of therapies for postmenopausal osteoporosis.Endocr Rev.2002;23(4):570578.
  55. Guyatt GH,Cranney A,Griffith L, et al.Summary of meta‐analyses of therapies for postmenopausal osteoporosis and the relationship between bone density and fractures.Endocrinol Metab Clin North Am.2002;31(3):659679, xii.
  56. Harris ST,Watts NB,Genant HK, et al.Effects of risedronate treatment on vertebral and nonvertebral fractures in women with postmenopausal osteoporosis: a randomized controlled trial. Vertebral Efficacy With Risedronate Therapy (VERT) Study Group.JAMA.1999;282(14):13441352.
  57. McClung MR,Geusens P,Miller PD, et al.Effect of risedronate on the risk of hip fracture in elderly women. Hip Intervention Program Study Group.N Engl J Med.2001;344(5):333340.
  58. Chesnut IC,Skag A,Christiansen C, et al.Effects of oral ibandronate administered daily or intermittently on fracture risk in postmenopausal osteoporosis.J Bone Miner Res.2004;19(8):12411249.
  59. Black DM,Delmas PD,Eastell R, et al.Once‐yearly zoledronic acid for treatment of postmenopausal osteoporosis.N Engl J Med. 32007;356(18):18091822.
  60. Rosen CJ.Clinical practice. Postmenopausal osteoporosis.N Engl J Med.2005;353(6):595603.
  61. American College of Rheumatology Ad Hoc Committee on Glucocorticoid‐Induced Osteoporosis.Recommendations for the prevention and treatment of glucocorticoid‐induced osteoporosis: 2001 update. [Review].Arthritis Rheum.2001;44(7):14961503.
  62. Edwards BJ,Bunta AD,Madison LD, et al.An osteoporosis and fracture intervention program increases the diagnosis and treatment for osteoporosis for patients with minimal trauma fractures.Jt Comm J Qual Patient Saf.2005;31(5):267274.
Issue
Journal of Hospital Medicine - 4(7)
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Journal of Hospital Medicine - 4(7)
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Acute vertebral fracture
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Acute vertebral fracture
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fracture, kyphoplasty, osteoporosis, osteoporosis fracture, spine fracture, vertebral compression fracture, vertebral fracture, vertebroplasty
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fracture, kyphoplasty, osteoporosis, osteoporosis fracture, spine fracture, vertebral compression fracture, vertebral fracture, vertebroplasty
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Northwestern Memorial Hospital, Division of Hospital Medicine, 251 E. Huron Street, Feinberg 16‐738, Chicago, IL 60611
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EHR Enhancements for Physician Assignment

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Systematically improving physician assignment during in‐hospital transitions of care by enhancing a preexisting hospital electronic health record

Preserving continuity of care and assuring patient safety are core values of the hospitalist movement.1 Communicating clinical information to the patient, the primary care provider (PCP), and hospital‐based providers has been recognized in the hospitalist literature as an important domain of quality assurance.29 Correctly identifying a patient's inpatient physician during admission and later in the hospital course is essential to achieve these goals. This initial step has not been specifically addressed in the literature, though the complexity of this process can create significant challenges for larger hospitalist programs.

Rochester General Hospital (RGH) is a 528‐bed community teaching hospital. Our hospitalist program evolved from the general medicine unit (GMU) faculty that traditionally cared for most unreferred patients; ie, those whose PCPs had no admitting privileges. In 2004, we began covering patients of privilege‐holder partnering PCPs. Within 3 years, more than 120 privileged PCPs decided to refer all their admissions to us, though unreferred cases still make up the majority of our 10,000 annual admissions. GMU hospitalists either round as teaching attendings or attend patients on nonteaching services. Many PCPs continue to admit their own patients.

As our program began to work with numerous partnering PCPs, it became difficult to decide which patients the hospitalist team should be called to admit, and which should be admitted to the PCP. Having the emergency department (ED) providers page the PCPs or their call partners of the PCPs for attending service decisions proved infeasible and unreliable. Additionally, similar challenges emerged later in the hospitalization when patients were transferred from one service, floor, or provider to another, especially from the intensive care unit (ICU) to the medical service.

Errors regarding admissions to the hospitalist service can be classified as:

  • Type‐I errors: The PCP provides inpatient care but the patient is erroneously admitted to a hospitalist, creating discontinuity of care and dissatisfaction.

  • Type‐II errors: The PCP refers to the hospitalists but is erroneously identified as the inpatient attending physician. As the hospitalist team is not notified about these cases, admitted patients may go without physician services for a period of time.

 

Methods

RGH has a widely used, clinically focused EHR system that does not offer full CPOE functionality. The EHR includes a database of physicians with admitting privileges, so we decided to store information on the system regarding PCPs' hospitalist coverage. Initially, we simply uploaded a list of our partnering PCPs. However, looking up information from files proved too cumbersome for busy ED providers and hospitalists. Additionally, this solution lacked a feedback loop to alert for errors.

Therefore, we designed a system with 3 main functional elements to identify and display each patient's:

  • candidacy for hospitalist coverage;

  • actual hospitalist coverage; and

  • mismatches between the above statuses.

 

These steps are explained below, followed by a description of the system's actual utilization. In addition to the text, we demonstrate the concept of and provide detailed information on our system in Figure 1 and Table 1.

Figure 1
Snapshot of the “Assignment Monitor” special‐function census is shown in the middle section. The top section describes the innovations, while the bottom section illustrates how cases on this alert list should be handled in the system (once the potential coverage errors are clarified and the patient's care is appropriately directed).
Assignment Monitor Census Algorithms and Definitions
Algorithms
This special census includes patients who meet any of the following criteria:
Attending physician is a GMU‐MD and GMU‐dr is {blank}
Attending physician is a GMU‐MD and GMU‐status is GMU‐cons, temp‐ICU, or non‐GMU
Attending physician is not a GMU‐MD and GMU‐dr is not {blank} and GMU‐status is not GMU‐cons, non‐GMU, or temp‐ICU
Attending physician is an ICU‐MD and patient's location is not ED or ICU and GMU‐status is not non‐GMU
Patient's status is INT (internal medicine in‐bed) and PCP has no privilege or is a GMU‐PCPCP and GMU‐dr is {blank} and Attending physician is not an ICU‐MD and GMU‐status is not temp‐ICU or non‐GMU
PCP has admission privilege and PCP is not a GMU‐PCPCP and GMU‐dr is not {blank} and GMU‐status is not non‐GMU or GMU+
GMU‐status is temp‐ICU and patient's location is not ED or ICU
GMU‐dr is {blank} and GMU‐status is GMU‐cons, GMU‐ALC or GMU+
GMU‐dr is ‐TBA, or ‐TRD and present time is between 8 AMand 4 PM
Definitions
Attending physician and PCP refers to the physicians assigned as attending and PCP, respectively in the core EHR system.
Physician categories are defined by listing all belonging members on centrally maintained, up‐to‐date databases:
GMU‐PCPCP: partnering community PCP physicians who refer their patients to the GMU hospitalist service
GMU‐MD: a GMU hospitalist attending physician
ICU‐MD: a critical care physician who attends in ICU, and automatically transfers most patients to hospitalists upon discharge from ICU
GMU‐dr signals acceptance to GMU hospitalist service, and names the hospitalist in the EHR. Temporary assignments (TBA or TRD) are used when the patient is accepted to the hospitalist program, but the rounding physician is not yet known (eg, a patient seen by the night hospitalist in the ED, or waiting reassignment from a weekend). Options include:
A physician's acronym (from last name and first initial) identifies the rounding hospitalist
TBA = to be assigned: accepted to GMU but not yet assigned to a rounding attending (used for new patients during evening and night hours while under the care of the on call team)
TRD = to redistribute from an attending's care who is leaving service (used for patients whose follow‐up coverage is distributed in the next morning by the call team)
A blank field means no hospitalist service for the patient
GMU‐status signals irregular rounding relationship with the patient. The following options are used:
Non‐GMU: patient will not be covered by GMU hospitalists (eg, signing off consult, admission to subspecialist service)
Temp‐ICU: patient will not be covered by GMU while in ICU (will automatically accept to GMU upon transfer to floor)
GMU‐cons: patient is on consultation service (GMU is not the attending service)
GMU‐ALC: patient is on alternative level of care (skilled nursing needs: no daily rounding by GMU hospitalist)
GMU+: patient is covered by GMU (despite the PCP not normally referring to GMU)

First, to identify a patient's candidacy for hospitalist coverage, we created a PCP color coding algorithm, based on whether the PCP has admitting privileges at RGH and/or has arranged hospitalist coverage for her/his patients. Whether or not hospitalist coverage is expected for a given patient's PCP is displayed on the EHR.

Second, a data field called GMU‐dr was created to describe whether the patient is actually assigned to a hospitalist. The GMU on‐call physician assigns a GMU‐dr to all appropriate patients (ie, updates the field's value). This step acknowledges hospitalist coverage for a given patient and also identifies the hospitalist physician. It simultaneously adds the patient to the appropriate rounding censuses. The GMU‐dr data field is updated every time the rounding physician changes (eg, for weekend coverage).

Third, the EHR's assignment monitor algorithm compares the expected and actual hospitalist coverage (based on PCP color coding, GMU‐dr, admission type, location, time of day, etc.) and displays mismatches on the assignment monitor census. We created this special‐function census to include patients with the more dangerous type‐II coverage errors that would not show up on our rounding censuses.

The assignment monitor census is regularly reviewed by the call physicians and should be cleared of patients. Unaccounted patients showing up on this census are handled with urgency equal to that of an unseen admission. Most patients on the assignment monitor census have missing or incorrect information: correcting that information removes the patient from this alert list. However, some patients with correct information are captured due to an unusual relationship with the hospitalist program; eg, consultation or individual exceptions from coverage arrangements. The GMU‐status field was created to explain such situations and remove these patients from the alert list.

Results

According to a survey that was distributed to the 19 eligible hospitalists and returned by 17 (89%), this system greatly improved our admission and patient distribution process, with the following results:

  • PCP color coding prevents type‐I and type‐II coverage identification errors more than once a week according to 94.1% and 88.2% of hospitalists, respectively. Ninety‐four percent agreed (absolutely or strongly) that color coding is a convenient tool to identify PCP referral status.

  • The assignment monitor identifies patients more than once a month that would have been lost in the preintervention era, according to 94.1% of the hospitalists. All hospitalists surveyed believed that this alert list had several times prevented potentially life‐threatening complications, and that the system is more useful than burdensome.

  • The GMU‐dr data field correctly identifies the hospitalist while the attending is misassigned in the core EHR system more than once a week, according to 93.7% of surveyed hospitalists (80% of these stated it happens daily). None believed the system would provide incorrect information with that frequency. Every hospitalist agreed that the GMU‐dr and GMU‐status tools are efficient methods for distributing patients, keeping track of attending designations, and maintaining a unit census and personal rounding censuses: cumulatively 85.7% absolutely agreed, while 12.7% strongly agreed.

 

We also assessed how promptly the call team responded to the assignment monitor alerts by correcting the information in the system (the census is recorded every 4 hours). Due to the intensity of the ED call, physicians often just scan this alert list (and take clinical action on the captured patients as needed) without updating the EHR fields until the end of their shifts. Measuring the speed of clearing patients from the census may grossly underestimate the actual usage of the list, though this data is useful to access a worst‐case scenario.

During the first week of September 2007 we cared for 270 patients: 52 were captured on the assignment monitor before a GMU‐dr was assigned or the patient was deemed non‐GMU. No patient was recaptured on the list more than 8 hours after the initial appearance.

Discussion

Since we enhanced a widely‐used program, our intervention required minimal training. As our innovation was designed and underwent trial by hospitalists, immediate feedback assured user‐friendly implementation, good acceptance, and improved workflow.

  • Color coding eliminated the time‐intensive and error‐prone lookup of coverage arrangements.

  • Updating GMU‐dr and GMU‐status in the EHR takes very little time and provides immediate benefits (eg, clearly defined rounding censuses). This task has been integrated into our signout tool, making these functions even more intuitive.

  • Using the assignment monitor census is fundamental for patient safety, but correcting EHR information creates some additional work for busy call physicians. Implementing this step required active change‐management: writing policies, designing metrics, and considering incentives. The call physicians (3 shifts per day) are officially responsible for patient distribution, including clearing the screening census before passing the call pager to the next shift.

 

We identified some potential limitations, though these issues generally apply to any information technology (IT) implementation: Setting up the program requires an adaptable EHR and close collaboration with the IT department. The system's accuracy depends on correctly identifying the patient's PCP in the EHR. Maintaining and coordinating the data regarding PCP privileges and hospitalist coverage requires a central database has been created.

Meanwhile, we found these tools very useful in solving problems beyond patient distribution:

  • PCP color coding can export information to other applications about hospitalist coverage and assist ED and nonmedical services to contact the proper medical service for admissions and consults.

  • GMU‐dr and GMU‐status can be used to create personal rounding censuses, provide billing lists to third‐party applications, and support proprietary applications, thus assisting patient distribution decisions and guiding hospital staff to call the patient's correct provider 24/7.

  • Special function censuses (defined by algorithms) are now used as alert lists for different patient issues (eg, observational patients staying beyond their allotted time) and by nonhospitalist services.

 

We presented hospitalist‐specific EHR concepts (patient coverage algorithms, special‐function censuses, and patient tracking by provider‐entered information) and their specific applications in our hospital. We believe our tools can be implemented in various locations and EHRs. Though the challenges may differ from setting to setting (eg, patient distribution between coexistent hospitalist programs, or responding to limitations of resident duty hours), these solutions are highly adaptable and have the potential of providing additional benefits beyond hospitalist coverage issues.

Acknowledgements

The authors thank Andrew Hakes for his invaluable assistance in graphic design; the authors also thank their information technology (IT) engineers and staff members, who programmed and are maintaining these functions in their EHR system, for their enthusiastic collaboration and for their assistance with the manuscript: Rich Prideaux, Robert Cawlfield, Kim Johnston, Brenda Ventrillo, and Katura Gardner.

References
  1. Pistoria M,Amin A,Dressler D,McKean S,Budnitz T.The core competencies in hospital medicine.J Hosp Med.2006;1(Suppl 1):8495.
  2. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19(6):624631.
  3. Wachter RM,Pantilat SZ.The “continuity visit” and the hospitalist model of care.Am J Med.2001;111(9B):40S42S.
  4. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831841.
  5. Goldman L,Pantilat SZ,Whitcomb WF.Passing the clinical baton: 6 principles to guide the hospitalist.Am J Med.2001;111(9B):36S39S.
  6. Nelson JR.The importance of postdischarge telephone follow‐up for hospitalists: a view from the trenches.Am J Med.2001;111(9B):43S44S.
  7. Stein J.The language of quality improvement: therapy classes.J Hosp Med.2006;1:327330.
  8. Coleman EA,Williams MV.Executing high‐quality care transitions: a call to do it right.J Hosp Med.2007;2:287290.
  9. Kripalani S,Jackson AT,Schnipper JL,Coleman EA.Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists.J Hosp Med.2007;2:314323.
Article PDF
Issue
Journal of Hospital Medicine - 4(5)
Page Number
308-312
Legacy Keywords
algorithms, communication, continuity of care, EHR, information technology, leadership, patient coverage/assignment, patient safety, system, teamwork, transition of care
Sections
Article PDF
Article PDF

Preserving continuity of care and assuring patient safety are core values of the hospitalist movement.1 Communicating clinical information to the patient, the primary care provider (PCP), and hospital‐based providers has been recognized in the hospitalist literature as an important domain of quality assurance.29 Correctly identifying a patient's inpatient physician during admission and later in the hospital course is essential to achieve these goals. This initial step has not been specifically addressed in the literature, though the complexity of this process can create significant challenges for larger hospitalist programs.

Rochester General Hospital (RGH) is a 528‐bed community teaching hospital. Our hospitalist program evolved from the general medicine unit (GMU) faculty that traditionally cared for most unreferred patients; ie, those whose PCPs had no admitting privileges. In 2004, we began covering patients of privilege‐holder partnering PCPs. Within 3 years, more than 120 privileged PCPs decided to refer all their admissions to us, though unreferred cases still make up the majority of our 10,000 annual admissions. GMU hospitalists either round as teaching attendings or attend patients on nonteaching services. Many PCPs continue to admit their own patients.

As our program began to work with numerous partnering PCPs, it became difficult to decide which patients the hospitalist team should be called to admit, and which should be admitted to the PCP. Having the emergency department (ED) providers page the PCPs or their call partners of the PCPs for attending service decisions proved infeasible and unreliable. Additionally, similar challenges emerged later in the hospitalization when patients were transferred from one service, floor, or provider to another, especially from the intensive care unit (ICU) to the medical service.

Errors regarding admissions to the hospitalist service can be classified as:

  • Type‐I errors: The PCP provides inpatient care but the patient is erroneously admitted to a hospitalist, creating discontinuity of care and dissatisfaction.

  • Type‐II errors: The PCP refers to the hospitalists but is erroneously identified as the inpatient attending physician. As the hospitalist team is not notified about these cases, admitted patients may go without physician services for a period of time.

 

Methods

RGH has a widely used, clinically focused EHR system that does not offer full CPOE functionality. The EHR includes a database of physicians with admitting privileges, so we decided to store information on the system regarding PCPs' hospitalist coverage. Initially, we simply uploaded a list of our partnering PCPs. However, looking up information from files proved too cumbersome for busy ED providers and hospitalists. Additionally, this solution lacked a feedback loop to alert for errors.

Therefore, we designed a system with 3 main functional elements to identify and display each patient's:

  • candidacy for hospitalist coverage;

  • actual hospitalist coverage; and

  • mismatches between the above statuses.

 

These steps are explained below, followed by a description of the system's actual utilization. In addition to the text, we demonstrate the concept of and provide detailed information on our system in Figure 1 and Table 1.

Figure 1
Snapshot of the “Assignment Monitor” special‐function census is shown in the middle section. The top section describes the innovations, while the bottom section illustrates how cases on this alert list should be handled in the system (once the potential coverage errors are clarified and the patient's care is appropriately directed).
Assignment Monitor Census Algorithms and Definitions
Algorithms
This special census includes patients who meet any of the following criteria:
Attending physician is a GMU‐MD and GMU‐dr is {blank}
Attending physician is a GMU‐MD and GMU‐status is GMU‐cons, temp‐ICU, or non‐GMU
Attending physician is not a GMU‐MD and GMU‐dr is not {blank} and GMU‐status is not GMU‐cons, non‐GMU, or temp‐ICU
Attending physician is an ICU‐MD and patient's location is not ED or ICU and GMU‐status is not non‐GMU
Patient's status is INT (internal medicine in‐bed) and PCP has no privilege or is a GMU‐PCPCP and GMU‐dr is {blank} and Attending physician is not an ICU‐MD and GMU‐status is not temp‐ICU or non‐GMU
PCP has admission privilege and PCP is not a GMU‐PCPCP and GMU‐dr is not {blank} and GMU‐status is not non‐GMU or GMU+
GMU‐status is temp‐ICU and patient's location is not ED or ICU
GMU‐dr is {blank} and GMU‐status is GMU‐cons, GMU‐ALC or GMU+
GMU‐dr is ‐TBA, or ‐TRD and present time is between 8 AMand 4 PM
Definitions
Attending physician and PCP refers to the physicians assigned as attending and PCP, respectively in the core EHR system.
Physician categories are defined by listing all belonging members on centrally maintained, up‐to‐date databases:
GMU‐PCPCP: partnering community PCP physicians who refer their patients to the GMU hospitalist service
GMU‐MD: a GMU hospitalist attending physician
ICU‐MD: a critical care physician who attends in ICU, and automatically transfers most patients to hospitalists upon discharge from ICU
GMU‐dr signals acceptance to GMU hospitalist service, and names the hospitalist in the EHR. Temporary assignments (TBA or TRD) are used when the patient is accepted to the hospitalist program, but the rounding physician is not yet known (eg, a patient seen by the night hospitalist in the ED, or waiting reassignment from a weekend). Options include:
A physician's acronym (from last name and first initial) identifies the rounding hospitalist
TBA = to be assigned: accepted to GMU but not yet assigned to a rounding attending (used for new patients during evening and night hours while under the care of the on call team)
TRD = to redistribute from an attending's care who is leaving service (used for patients whose follow‐up coverage is distributed in the next morning by the call team)
A blank field means no hospitalist service for the patient
GMU‐status signals irregular rounding relationship with the patient. The following options are used:
Non‐GMU: patient will not be covered by GMU hospitalists (eg, signing off consult, admission to subspecialist service)
Temp‐ICU: patient will not be covered by GMU while in ICU (will automatically accept to GMU upon transfer to floor)
GMU‐cons: patient is on consultation service (GMU is not the attending service)
GMU‐ALC: patient is on alternative level of care (skilled nursing needs: no daily rounding by GMU hospitalist)
GMU+: patient is covered by GMU (despite the PCP not normally referring to GMU)

First, to identify a patient's candidacy for hospitalist coverage, we created a PCP color coding algorithm, based on whether the PCP has admitting privileges at RGH and/or has arranged hospitalist coverage for her/his patients. Whether or not hospitalist coverage is expected for a given patient's PCP is displayed on the EHR.

Second, a data field called GMU‐dr was created to describe whether the patient is actually assigned to a hospitalist. The GMU on‐call physician assigns a GMU‐dr to all appropriate patients (ie, updates the field's value). This step acknowledges hospitalist coverage for a given patient and also identifies the hospitalist physician. It simultaneously adds the patient to the appropriate rounding censuses. The GMU‐dr data field is updated every time the rounding physician changes (eg, for weekend coverage).

Third, the EHR's assignment monitor algorithm compares the expected and actual hospitalist coverage (based on PCP color coding, GMU‐dr, admission type, location, time of day, etc.) and displays mismatches on the assignment monitor census. We created this special‐function census to include patients with the more dangerous type‐II coverage errors that would not show up on our rounding censuses.

The assignment monitor census is regularly reviewed by the call physicians and should be cleared of patients. Unaccounted patients showing up on this census are handled with urgency equal to that of an unseen admission. Most patients on the assignment monitor census have missing or incorrect information: correcting that information removes the patient from this alert list. However, some patients with correct information are captured due to an unusual relationship with the hospitalist program; eg, consultation or individual exceptions from coverage arrangements. The GMU‐status field was created to explain such situations and remove these patients from the alert list.

Results

According to a survey that was distributed to the 19 eligible hospitalists and returned by 17 (89%), this system greatly improved our admission and patient distribution process, with the following results:

  • PCP color coding prevents type‐I and type‐II coverage identification errors more than once a week according to 94.1% and 88.2% of hospitalists, respectively. Ninety‐four percent agreed (absolutely or strongly) that color coding is a convenient tool to identify PCP referral status.

  • The assignment monitor identifies patients more than once a month that would have been lost in the preintervention era, according to 94.1% of the hospitalists. All hospitalists surveyed believed that this alert list had several times prevented potentially life‐threatening complications, and that the system is more useful than burdensome.

  • The GMU‐dr data field correctly identifies the hospitalist while the attending is misassigned in the core EHR system more than once a week, according to 93.7% of surveyed hospitalists (80% of these stated it happens daily). None believed the system would provide incorrect information with that frequency. Every hospitalist agreed that the GMU‐dr and GMU‐status tools are efficient methods for distributing patients, keeping track of attending designations, and maintaining a unit census and personal rounding censuses: cumulatively 85.7% absolutely agreed, while 12.7% strongly agreed.

 

We also assessed how promptly the call team responded to the assignment monitor alerts by correcting the information in the system (the census is recorded every 4 hours). Due to the intensity of the ED call, physicians often just scan this alert list (and take clinical action on the captured patients as needed) without updating the EHR fields until the end of their shifts. Measuring the speed of clearing patients from the census may grossly underestimate the actual usage of the list, though this data is useful to access a worst‐case scenario.

During the first week of September 2007 we cared for 270 patients: 52 were captured on the assignment monitor before a GMU‐dr was assigned or the patient was deemed non‐GMU. No patient was recaptured on the list more than 8 hours after the initial appearance.

Discussion

Since we enhanced a widely‐used program, our intervention required minimal training. As our innovation was designed and underwent trial by hospitalists, immediate feedback assured user‐friendly implementation, good acceptance, and improved workflow.

  • Color coding eliminated the time‐intensive and error‐prone lookup of coverage arrangements.

  • Updating GMU‐dr and GMU‐status in the EHR takes very little time and provides immediate benefits (eg, clearly defined rounding censuses). This task has been integrated into our signout tool, making these functions even more intuitive.

  • Using the assignment monitor census is fundamental for patient safety, but correcting EHR information creates some additional work for busy call physicians. Implementing this step required active change‐management: writing policies, designing metrics, and considering incentives. The call physicians (3 shifts per day) are officially responsible for patient distribution, including clearing the screening census before passing the call pager to the next shift.

 

We identified some potential limitations, though these issues generally apply to any information technology (IT) implementation: Setting up the program requires an adaptable EHR and close collaboration with the IT department. The system's accuracy depends on correctly identifying the patient's PCP in the EHR. Maintaining and coordinating the data regarding PCP privileges and hospitalist coverage requires a central database has been created.

Meanwhile, we found these tools very useful in solving problems beyond patient distribution:

  • PCP color coding can export information to other applications about hospitalist coverage and assist ED and nonmedical services to contact the proper medical service for admissions and consults.

  • GMU‐dr and GMU‐status can be used to create personal rounding censuses, provide billing lists to third‐party applications, and support proprietary applications, thus assisting patient distribution decisions and guiding hospital staff to call the patient's correct provider 24/7.

  • Special function censuses (defined by algorithms) are now used as alert lists for different patient issues (eg, observational patients staying beyond their allotted time) and by nonhospitalist services.

 

We presented hospitalist‐specific EHR concepts (patient coverage algorithms, special‐function censuses, and patient tracking by provider‐entered information) and their specific applications in our hospital. We believe our tools can be implemented in various locations and EHRs. Though the challenges may differ from setting to setting (eg, patient distribution between coexistent hospitalist programs, or responding to limitations of resident duty hours), these solutions are highly adaptable and have the potential of providing additional benefits beyond hospitalist coverage issues.

Acknowledgements

The authors thank Andrew Hakes for his invaluable assistance in graphic design; the authors also thank their information technology (IT) engineers and staff members, who programmed and are maintaining these functions in their EHR system, for their enthusiastic collaboration and for their assistance with the manuscript: Rich Prideaux, Robert Cawlfield, Kim Johnston, Brenda Ventrillo, and Katura Gardner.

Preserving continuity of care and assuring patient safety are core values of the hospitalist movement.1 Communicating clinical information to the patient, the primary care provider (PCP), and hospital‐based providers has been recognized in the hospitalist literature as an important domain of quality assurance.29 Correctly identifying a patient's inpatient physician during admission and later in the hospital course is essential to achieve these goals. This initial step has not been specifically addressed in the literature, though the complexity of this process can create significant challenges for larger hospitalist programs.

Rochester General Hospital (RGH) is a 528‐bed community teaching hospital. Our hospitalist program evolved from the general medicine unit (GMU) faculty that traditionally cared for most unreferred patients; ie, those whose PCPs had no admitting privileges. In 2004, we began covering patients of privilege‐holder partnering PCPs. Within 3 years, more than 120 privileged PCPs decided to refer all their admissions to us, though unreferred cases still make up the majority of our 10,000 annual admissions. GMU hospitalists either round as teaching attendings or attend patients on nonteaching services. Many PCPs continue to admit their own patients.

As our program began to work with numerous partnering PCPs, it became difficult to decide which patients the hospitalist team should be called to admit, and which should be admitted to the PCP. Having the emergency department (ED) providers page the PCPs or their call partners of the PCPs for attending service decisions proved infeasible and unreliable. Additionally, similar challenges emerged later in the hospitalization when patients were transferred from one service, floor, or provider to another, especially from the intensive care unit (ICU) to the medical service.

Errors regarding admissions to the hospitalist service can be classified as:

  • Type‐I errors: The PCP provides inpatient care but the patient is erroneously admitted to a hospitalist, creating discontinuity of care and dissatisfaction.

  • Type‐II errors: The PCP refers to the hospitalists but is erroneously identified as the inpatient attending physician. As the hospitalist team is not notified about these cases, admitted patients may go without physician services for a period of time.

 

Methods

RGH has a widely used, clinically focused EHR system that does not offer full CPOE functionality. The EHR includes a database of physicians with admitting privileges, so we decided to store information on the system regarding PCPs' hospitalist coverage. Initially, we simply uploaded a list of our partnering PCPs. However, looking up information from files proved too cumbersome for busy ED providers and hospitalists. Additionally, this solution lacked a feedback loop to alert for errors.

Therefore, we designed a system with 3 main functional elements to identify and display each patient's:

  • candidacy for hospitalist coverage;

  • actual hospitalist coverage; and

  • mismatches between the above statuses.

 

These steps are explained below, followed by a description of the system's actual utilization. In addition to the text, we demonstrate the concept of and provide detailed information on our system in Figure 1 and Table 1.

Figure 1
Snapshot of the “Assignment Monitor” special‐function census is shown in the middle section. The top section describes the innovations, while the bottom section illustrates how cases on this alert list should be handled in the system (once the potential coverage errors are clarified and the patient's care is appropriately directed).
Assignment Monitor Census Algorithms and Definitions
Algorithms
This special census includes patients who meet any of the following criteria:
Attending physician is a GMU‐MD and GMU‐dr is {blank}
Attending physician is a GMU‐MD and GMU‐status is GMU‐cons, temp‐ICU, or non‐GMU
Attending physician is not a GMU‐MD and GMU‐dr is not {blank} and GMU‐status is not GMU‐cons, non‐GMU, or temp‐ICU
Attending physician is an ICU‐MD and patient's location is not ED or ICU and GMU‐status is not non‐GMU
Patient's status is INT (internal medicine in‐bed) and PCP has no privilege or is a GMU‐PCPCP and GMU‐dr is {blank} and Attending physician is not an ICU‐MD and GMU‐status is not temp‐ICU or non‐GMU
PCP has admission privilege and PCP is not a GMU‐PCPCP and GMU‐dr is not {blank} and GMU‐status is not non‐GMU or GMU+
GMU‐status is temp‐ICU and patient's location is not ED or ICU
GMU‐dr is {blank} and GMU‐status is GMU‐cons, GMU‐ALC or GMU+
GMU‐dr is ‐TBA, or ‐TRD and present time is between 8 AMand 4 PM
Definitions
Attending physician and PCP refers to the physicians assigned as attending and PCP, respectively in the core EHR system.
Physician categories are defined by listing all belonging members on centrally maintained, up‐to‐date databases:
GMU‐PCPCP: partnering community PCP physicians who refer their patients to the GMU hospitalist service
GMU‐MD: a GMU hospitalist attending physician
ICU‐MD: a critical care physician who attends in ICU, and automatically transfers most patients to hospitalists upon discharge from ICU
GMU‐dr signals acceptance to GMU hospitalist service, and names the hospitalist in the EHR. Temporary assignments (TBA or TRD) are used when the patient is accepted to the hospitalist program, but the rounding physician is not yet known (eg, a patient seen by the night hospitalist in the ED, or waiting reassignment from a weekend). Options include:
A physician's acronym (from last name and first initial) identifies the rounding hospitalist
TBA = to be assigned: accepted to GMU but not yet assigned to a rounding attending (used for new patients during evening and night hours while under the care of the on call team)
TRD = to redistribute from an attending's care who is leaving service (used for patients whose follow‐up coverage is distributed in the next morning by the call team)
A blank field means no hospitalist service for the patient
GMU‐status signals irregular rounding relationship with the patient. The following options are used:
Non‐GMU: patient will not be covered by GMU hospitalists (eg, signing off consult, admission to subspecialist service)
Temp‐ICU: patient will not be covered by GMU while in ICU (will automatically accept to GMU upon transfer to floor)
GMU‐cons: patient is on consultation service (GMU is not the attending service)
GMU‐ALC: patient is on alternative level of care (skilled nursing needs: no daily rounding by GMU hospitalist)
GMU+: patient is covered by GMU (despite the PCP not normally referring to GMU)

First, to identify a patient's candidacy for hospitalist coverage, we created a PCP color coding algorithm, based on whether the PCP has admitting privileges at RGH and/or has arranged hospitalist coverage for her/his patients. Whether or not hospitalist coverage is expected for a given patient's PCP is displayed on the EHR.

Second, a data field called GMU‐dr was created to describe whether the patient is actually assigned to a hospitalist. The GMU on‐call physician assigns a GMU‐dr to all appropriate patients (ie, updates the field's value). This step acknowledges hospitalist coverage for a given patient and also identifies the hospitalist physician. It simultaneously adds the patient to the appropriate rounding censuses. The GMU‐dr data field is updated every time the rounding physician changes (eg, for weekend coverage).

Third, the EHR's assignment monitor algorithm compares the expected and actual hospitalist coverage (based on PCP color coding, GMU‐dr, admission type, location, time of day, etc.) and displays mismatches on the assignment monitor census. We created this special‐function census to include patients with the more dangerous type‐II coverage errors that would not show up on our rounding censuses.

The assignment monitor census is regularly reviewed by the call physicians and should be cleared of patients. Unaccounted patients showing up on this census are handled with urgency equal to that of an unseen admission. Most patients on the assignment monitor census have missing or incorrect information: correcting that information removes the patient from this alert list. However, some patients with correct information are captured due to an unusual relationship with the hospitalist program; eg, consultation or individual exceptions from coverage arrangements. The GMU‐status field was created to explain such situations and remove these patients from the alert list.

Results

According to a survey that was distributed to the 19 eligible hospitalists and returned by 17 (89%), this system greatly improved our admission and patient distribution process, with the following results:

  • PCP color coding prevents type‐I and type‐II coverage identification errors more than once a week according to 94.1% and 88.2% of hospitalists, respectively. Ninety‐four percent agreed (absolutely or strongly) that color coding is a convenient tool to identify PCP referral status.

  • The assignment monitor identifies patients more than once a month that would have been lost in the preintervention era, according to 94.1% of the hospitalists. All hospitalists surveyed believed that this alert list had several times prevented potentially life‐threatening complications, and that the system is more useful than burdensome.

  • The GMU‐dr data field correctly identifies the hospitalist while the attending is misassigned in the core EHR system more than once a week, according to 93.7% of surveyed hospitalists (80% of these stated it happens daily). None believed the system would provide incorrect information with that frequency. Every hospitalist agreed that the GMU‐dr and GMU‐status tools are efficient methods for distributing patients, keeping track of attending designations, and maintaining a unit census and personal rounding censuses: cumulatively 85.7% absolutely agreed, while 12.7% strongly agreed.

 

We also assessed how promptly the call team responded to the assignment monitor alerts by correcting the information in the system (the census is recorded every 4 hours). Due to the intensity of the ED call, physicians often just scan this alert list (and take clinical action on the captured patients as needed) without updating the EHR fields until the end of their shifts. Measuring the speed of clearing patients from the census may grossly underestimate the actual usage of the list, though this data is useful to access a worst‐case scenario.

During the first week of September 2007 we cared for 270 patients: 52 were captured on the assignment monitor before a GMU‐dr was assigned or the patient was deemed non‐GMU. No patient was recaptured on the list more than 8 hours after the initial appearance.

Discussion

Since we enhanced a widely‐used program, our intervention required minimal training. As our innovation was designed and underwent trial by hospitalists, immediate feedback assured user‐friendly implementation, good acceptance, and improved workflow.

  • Color coding eliminated the time‐intensive and error‐prone lookup of coverage arrangements.

  • Updating GMU‐dr and GMU‐status in the EHR takes very little time and provides immediate benefits (eg, clearly defined rounding censuses). This task has been integrated into our signout tool, making these functions even more intuitive.

  • Using the assignment monitor census is fundamental for patient safety, but correcting EHR information creates some additional work for busy call physicians. Implementing this step required active change‐management: writing policies, designing metrics, and considering incentives. The call physicians (3 shifts per day) are officially responsible for patient distribution, including clearing the screening census before passing the call pager to the next shift.

 

We identified some potential limitations, though these issues generally apply to any information technology (IT) implementation: Setting up the program requires an adaptable EHR and close collaboration with the IT department. The system's accuracy depends on correctly identifying the patient's PCP in the EHR. Maintaining and coordinating the data regarding PCP privileges and hospitalist coverage requires a central database has been created.

Meanwhile, we found these tools very useful in solving problems beyond patient distribution:

  • PCP color coding can export information to other applications about hospitalist coverage and assist ED and nonmedical services to contact the proper medical service for admissions and consults.

  • GMU‐dr and GMU‐status can be used to create personal rounding censuses, provide billing lists to third‐party applications, and support proprietary applications, thus assisting patient distribution decisions and guiding hospital staff to call the patient's correct provider 24/7.

  • Special function censuses (defined by algorithms) are now used as alert lists for different patient issues (eg, observational patients staying beyond their allotted time) and by nonhospitalist services.

 

We presented hospitalist‐specific EHR concepts (patient coverage algorithms, special‐function censuses, and patient tracking by provider‐entered information) and their specific applications in our hospital. We believe our tools can be implemented in various locations and EHRs. Though the challenges may differ from setting to setting (eg, patient distribution between coexistent hospitalist programs, or responding to limitations of resident duty hours), these solutions are highly adaptable and have the potential of providing additional benefits beyond hospitalist coverage issues.

Acknowledgements

The authors thank Andrew Hakes for his invaluable assistance in graphic design; the authors also thank their information technology (IT) engineers and staff members, who programmed and are maintaining these functions in their EHR system, for their enthusiastic collaboration and for their assistance with the manuscript: Rich Prideaux, Robert Cawlfield, Kim Johnston, Brenda Ventrillo, and Katura Gardner.

References
  1. Pistoria M,Amin A,Dressler D,McKean S,Budnitz T.The core competencies in hospital medicine.J Hosp Med.2006;1(Suppl 1):8495.
  2. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19(6):624631.
  3. Wachter RM,Pantilat SZ.The “continuity visit” and the hospitalist model of care.Am J Med.2001;111(9B):40S42S.
  4. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831841.
  5. Goldman L,Pantilat SZ,Whitcomb WF.Passing the clinical baton: 6 principles to guide the hospitalist.Am J Med.2001;111(9B):36S39S.
  6. Nelson JR.The importance of postdischarge telephone follow‐up for hospitalists: a view from the trenches.Am J Med.2001;111(9B):43S44S.
  7. Stein J.The language of quality improvement: therapy classes.J Hosp Med.2006;1:327330.
  8. Coleman EA,Williams MV.Executing high‐quality care transitions: a call to do it right.J Hosp Med.2007;2:287290.
  9. Kripalani S,Jackson AT,Schnipper JL,Coleman EA.Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists.J Hosp Med.2007;2:314323.
References
  1. Pistoria M,Amin A,Dressler D,McKean S,Budnitz T.The core competencies in hospital medicine.J Hosp Med.2006;1(Suppl 1):8495.
  2. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19(6):624631.
  3. Wachter RM,Pantilat SZ.The “continuity visit” and the hospitalist model of care.Am J Med.2001;111(9B):40S42S.
  4. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831841.
  5. Goldman L,Pantilat SZ,Whitcomb WF.Passing the clinical baton: 6 principles to guide the hospitalist.Am J Med.2001;111(9B):36S39S.
  6. Nelson JR.The importance of postdischarge telephone follow‐up for hospitalists: a view from the trenches.Am J Med.2001;111(9B):43S44S.
  7. Stein J.The language of quality improvement: therapy classes.J Hosp Med.2006;1:327330.
  8. Coleman EA,Williams MV.Executing high‐quality care transitions: a call to do it right.J Hosp Med.2007;2:287290.
  9. Kripalani S,Jackson AT,Schnipper JL,Coleman EA.Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists.J Hosp Med.2007;2:314323.
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Journal of Hospital Medicine - 4(5)
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Systematically improving physician assignment during in‐hospital transitions of care by enhancing a preexisting hospital electronic health record
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Systematically improving physician assignment during in‐hospital transitions of care by enhancing a preexisting hospital electronic health record
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algorithms, communication, continuity of care, EHR, information technology, leadership, patient coverage/assignment, patient safety, system, teamwork, transition of care
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algorithms, communication, continuity of care, EHR, information technology, leadership, patient coverage/assignment, patient safety, system, teamwork, transition of care
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Hospitalist‐Run Short‐Stay Unit

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A hospitalist‐run short‐stay unit: Features that predict length‐of‐stay and eventual admission to traditional inpatient services

Short‐stay units (SSUs) are common alternatives to traditional inpatient services.1 When defined broadly to include observation units for low‐risk chest pain patients, SSUs exist in one‐third of hospitals in the United States.2 Amidst growing demands for inpatient services, SSUs have recently developed beyond observation medicine to provide more complex inpatient services in locations commonly adjacent to emergency departments (EDs).1 Hospitalists are well‐positioned to staff these emerging SSUs because of their expertise in managing complex inpatient services.3

Despite this, we found only 3 reports of hospitalist‐run SSUs designed for general medical inpatients (2 from Spain and 1 from Canada).46 Whereas these early reports introduce hospitalist‐run SSUs, they provide limited data to make firm conclusions about their usefulness or appropriate design. For example, none of these reports assessed patients' characteristics upon admission. Nor did they provide details about the services that the SSUs provided. Yet evaluation of both types of patient‐level datadescriptions of patients' needs upon admission and how these needs are met during their staysdetermine whether or not hospitalist‐run SSUs meet their potential to efficiently care for backlogs of patients who otherwise await admission to traditional inpatient services.

In order to further explore these issues, we first sought to characterize our SSU patients upon admission and record what services they received during their stays. To help interpret our results, we then investigated associations between these characteristics and measures of successfully caring for patients in our SSU.

Patients and Methods

Design and Setting

In this prospective cohort study, we included all patients admitted to the hospitalist‐run SSU of Cook County Hospital, a 500‐bed public teaching hospital in Chicago, Illinois, from January through April of 2006. Our 14‐bed SSU opened in 2002 to reduce overcrowding on the traditional inpatient wards by admitting adult patients who require inpatient care but might be eligible for discharge within 3 days. The unit is geographically part of the ED but is staffed by resident physicians and a rotating group of hospitalist attending physicians from the Department of Medicine. At least 1 attending and resident physician are available throughout the day, including weekend days and holidays; evenings are covered by a resident who presents overnight admissions to an attending physician the following morning.

ED physicians admit general medical patients to the SSU 24 hours per day, 7 days per week. Though admissions do not require prior approval from SSU physicians, the Departments of Medicine and Emergency Medicine have collaboratively promoted 5 suggested admission‐location guidelines to admitting ED physicians (Figure 1). For candidate SSU patients, these 5 guidelines are not intended to be restrictive but to provide a framework for the complex decision‐making process that our ED physicians encounter, particularly during periods of extreme overcrowding.7 First, patients should have an anticipated stay shorter than 72 hours. Second, patients should not have an eventual need for admission to traditional inpatient services such as the general medicine wards or intensive care units; this guideline is intended to improve patient safety by reducing unnecessary handoffs between physicians.8 Third, patients with provisional cardiovascular diagnoses should be preferentially admitted to the SSU over the general medical wards; this guideline is intended to improve hospital‐wide efficiency because our SSU is equipped with continuous telemetry monitors, an exercise treadmill testing (ETT) laboratory, and other reserved cardiac tests (see Admission Characteristics and Services Received section, below). Fourth, patients' risk should be no higher than intermediate level. Admitting ED physicians are encouraged to use posted risk estimators for patients with provisional diagnoses of possible acute coronary syndrome (ACS), decompensated heart failure, asthma exacerbation, and out‐of‐control diabetes. Finally, patients should not need advanced ancillary services; these include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements).

Figure 1
Flow diagram and suggested admission‐location guidelines for general medicine patients who require overnight hospital stays. Flow begins at the base of the figure, which represents the major point‐of‐entry for 90% of our patients in to the hospital: the emergency department. Widths of arrows are approximately proportional to the flow of patients from the ED to other patient care units. aAdvanced ancillary services include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements). Abbreviations: ACS, acute coronary syndrome; ED, emergency department; LOS, length‐of‐stay; PAs, physician assistants.

Subjects

The study subjects were all patients admitted to the SSU during the 4‐month study period. Patients were excluded from the entire study if they refused verbal consent to participate. All patients who consented were included in the description of patient admission characteristics. Thirteen patients who prematurely left the SSU against medical advice, however, were neither included in the descriptions of services received nor in the analyses of predictors of successful SSU stays. We excluded these patients because they needed services that they did not receiveincluding these patients in our analysis would tend to overestimate the efficiency of our SSU by shortening the length‐of‐stay (LOS) without adding diagnostic tests or treatments.

Data Collection

After receiving approval from the institutional review board, attending physician investigators conducted an interview, physical examination, and review of medical records for each enrolled patient within 12 hours of admission to the SSU. When ED attending physicians' provisional primary diagnoses included possible ACS or decompensated heart failure, which we knew from earlier pilot data were our 2 most common provisional diagnoses, investigators gathered patient data to be applied in validated models of risk after the study period (Figure 2).9, 10 Some of the clinical predictors required for these models are based on patients' findings on presentation to the ED. For example, Goldman's risk model for major cardiac events uses patients' initial systolic blood pressures on presentation to the ED.9 In such cases, investigators gathered needed data from electronic and paper charts generated in the ED. Upon discharge from the SSU, investigators reviewed patients' medical records a second time. All data were entered by investigators and instantly committed into an online database.

Figure 2
Provisional diagnostic groupings and risk assessments upon admission to the SSU during the 4‐month study period. aNoncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis. bThe validated risk model of Goldman et al.9 predicts major cardiac events in 72 hours. Major cardiac events include ventricular fibrillation, cardiac arrest, new complete heart block, insertion of a temporary pacemaker, emergent cardioversion, cardiogenic shock, use of an intraaortic balloon pump, endotracheal intubation, and recurrent ischemic chest pain requiring urgent coronary angiography and urgent revascularization. cThe validated risk model of Fonarow et al.10 predicts in‐hospital mortality rate. The original model classified patients into 5 risk stratums. We modified the model by combining their lowest intermediate risk stratums (“intermediate 3” and “intermediate 2”), which had similar crude mortality rates in their validation cohorts, in to a single “low‐risk” stratum. dOther cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease. eThe 14 patients with possible ACS who were high risk all had electrocardiographic findings that were both not known to be old and were suggestive of ST‐segment elevation myocardial infarction. Yet upon admission to the SSU, all of these 14 patients had 2 negative serum troponin I tests that were drawn 8 hours apart (data not shown), suggesting that their electrocardiographic findings were in fact old. Abbreviation: ACS, acute coronary syndrome.

Admission Characteristics and Services Received

Patients were grouped according to the provisional diagnoses of ED attending physicians upon admission to the SSU (Figure 2). We chose to group patients by the provisional diagnoses of EDnot SSUattending physicians to better understand how ED physicians, the physicians who make the admission‐location decisions in our hospital, were using the SSU. Patients were first grouped as having possible ACS or heart failure, because patients with these provisional diagnoses were preferentially admitted to the SSU (Figure 1). When neither diagnosis was listed, patients were grouped according to ED attending physicians' first‐listed diagnoses. At the end of the study period, relevant risk models were applied to patients with possible ACS or heart failure and stratified as very low, low, intermediate, or high risk.9, 10 Patients with both possible ACS and heart failure were grouped according to the diagnosis with the highest corresponding risk assessment; if both risk assessments were the same, then the first‐listed diagnosis was used. Though developed to predict different clinical outcomes during different time periods, risk strata from the corresponding risk models were pooled across both diagnoses to develop a risk summary.

Upon discharge, investigators recorded which advanced diagnostic tests, specialty consultations, and acute care treatments patients received while in the SSU. Diagnostic tests were considered advanced if they were not routinely performed within 2 hours of being ordered. Advanced diagnostic tests were grouped into 2 types by their accessibility to ordering SSU physicians. Open access tests included echocardiograms and ETTs, which were reserved for SSU patients 6 days per week. Though the availability of open access tests was not unlimited, ordering physicians' needs for them rarely exceeded the immediate supply. On the other hand, limited access tests included both cardiac stress imaging studies, which were reserved for SSU patients on a very limited basis 4 or 5 days per week, and other tests that were not reserved for SSU patients, such as endoscopy, magnetic resonance imaging, or ultrasonography. Ordering physicians' needs for limited access tests often exceeded their immediate supply; in such cases, SSU patients were placed without priority into queues that included patients from the entire hospital.

Investigators recorded when patients received advanced diagnostic tests that were ordered by specialists. These tests, however, were not included in analyses of how services received by SSU patients affected SSU success, because SSU attending physicians were only indirectly involved in whether or not patients received these tests. Treatments were considered acute care treatments if they were commonly administered only in acute care settings, such as heparin for unstable angina or intravenous furosemide for pulmonary edema.

SSU Success

The SSU was designed to care for patients during brief stays and without eventual admission to traditional inpatient services. Therefore, we used patients' LOS and whether or not patients were admitted to traditional inpatient services as measures of SSU success. LOS was calculated from the time patients arrived in the SSU until the time they left. Therefore, neither time spent in the ED before admission to the SSU nor time spent on traditional inpatient services (if needed) contributed to our definition of LOS. Individual SSU patients were considered successfully cared for in the SSU if their LOS was less than 72 hours and they were discharged directly home from the SSU. We explored associations between these outcomes and provisional diagnoses, risk assessments, and services received.

Data Analysis

LOS data were right‐skewed; therefore, we used the Mann‐Whitney test for comparisons between 2 groups and the Kruskal‐Wallis test for comparisons among 3 or more groups. To test for trends of median LOS among ordered groupings, we used the method of Cuzick.11 We used Pearson's chi‐square test to compare proportions of patients grouped into categories and the chi‐square test for trends with equal scoring to test for trends among ordered groupings.

We performed multiple logistic regression to explore which variables were associated with SSU success. The following 5 demographic variables from Table 1 were insignificant in all single‐variable and multiple‐variable regression models that we tested and were, therefore, removed from further analyses to create more parsimonious models: gender, language, ethnicity, race, and whether or not patients had a primary care provider. Our multiple logistic regression models were fitted by maximum likelihood methods. In all of these models, odds ratios (ORs) were adjusted for patient characteristics that included age (in years), insulin‐requiring diabetes mellitus (yes or no), SSU attending physician, day of the week of SSU admission (weekday or weekend), and hospitalization during the preceding year. Confidence intervals (CIs) for predicted probabilities were computed using the delta method. All analyses were conducted with Stata Statistical Software, Release 9 (StataCorp, College Station, TX).

Admission Characteristics of Enrolled Short‐Stay Unit Patients
  • NOTE: n = 751. Values are n (%) unless otherwise indicated.

  • Emergency department physicians listed an additional provisional diagnosis for 186 patients (25% of 751).

  • Other cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease.

  • Noncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis.

Mean age, years (SD) (25th‐75th percentiles)58 (12) (49‐66)
Men389 (52)
Lacking a primary care provider256 (34)
Non‐English speaking217 (29)
Ethnicity is Hispanic or Latino105 (14)
Race is Black or African‐American480 (64)
Hospitalized within the preceding year322 (43)
Insulin‐requiring diabetes mellitus83 (11)
Previous coronary artery revascularization89 (12)
Provisional diagnosis* 
Possible acute coronary syndrome427 (57)
Heart failure214 (29)
Other cardiovascular62 (8)
Noncardiovascular48 (6)

Results

Subjects

During the 4‐month study period, 755 patients were admitted to the SSU. Among these patients, 4 were excluded from our study because they refused verbal consent. In the remaining study sample of 751 patients, all were included in the descriptions of patients' admission characteristics (Table 1), but 13 patients who left prematurely were excluded in both the descriptions of services received (Table 2) and the analyses of SSU success (Tables 3 and 4).

Services Received by Provisional Diagnosis
Service receivedPossible ACS n = 418 (%)Heart Failure n = 211 (%)Other Cardiovascular n = 61 (%)Noncardiovascular n = 48 (%)Total n = 738 (%)
  • NOTE: n = 738. Does not include 13 patients who prematurely left the SSU against medical advice. P values for Pearson's chi‐square test for differences in proportions across all four groups were all <0.001.

  • Abbreviations: ACS, acute coronary syndrome; ETT, exercise treadmill test.

  • Eighteen patients received both a resting echocardiogram and an ETT.

  • Two patients received both a stress imaging test and another limited access test. Other limited access tests included esophagogastroduodenoscopy, colonoscopy, brain or spine magnetic resonance imaging, abdominal ultrasonography, carotid artery ultrasonography, cardiac multiple gated acquisition scan, bone scintigraphy, cardiac pacemaker interrogation, pulmonary angiography, and ventilation‐perfusion scan; 6 patients received 2 such tests.

  • Stress imaging tests included myocardial perfusion imaging and stress echocardiography; 3 patients received 2 stress imaging studies.

  • Acute care treatments included intravenous furosemide, nebulized albuterol or ipratropium, treatment doses of heparin, intravenous antibiotics, and intravenous insulin; 10 patients received 2 or more acute care treatments.

  • Additional diagnostic tests that required arrangement by specialty consultants were not considered open or limited access tests. They included coronary angiography, transesophageal echocardiography, cardiac electrophysiology study, and electroencephalography.

Open access test*3759561343
Resting     
Echocardiography2959561339
ETT120507
Limited access test24810817
Stress imaging2157214
Acute care treatment227856039
Specialty consultation241220819
Any above service6893676975
Patient Outcome by Provisional Diagnosis and Services Received
Provisional Diagnosis and Services ReceivednMedian LOS [hours (IQR)]Stay Longer than 72 Hours (%)Admission to Traditional Inpatient Service (%)Stay Longer than 72 Hours or Admission to Traditional Inpatient Service (%)
  • NOTE: n = 738. Does not include 13 patients who left prematurely against medical advice. P values are for Pearson's chi‐square test unless otherwise indicated.

  • Abbreviations: ACS, acute coronary syndrome; CV, cardiovascular; IQR, interquartile range; LOS, length‐of‐stay.

  • Among the 9% of patients (66/738) who were admitted to the traditional inpatient services, SSU attending physicians' reasons for admission for 74% of patients (49/66) were to provide treatments (n = 32), diagnostic tests (n = 14), and ancillary services (n = 3) not provided in the SSU or because prolonged treatment courses were anticipated on the current therapies (n = 17). Prior to admission to traditional inpatient services, 21 patients (32% of 66) had received no advanced diagnostic tests, and 9 patients (14% of 66) had received no advanced diagnostic tests, acute care treatments, or specialty consultations.

  • P values are for differences among provisional diagnostic groupings.

  • Nonparametric Kruskal‐Wallis test.

  • Nonparametric Mann‐Whitney test.

  • Among the 184 patients who did not receive any of the above services, 172 (23% of 738) stayed less than 72 hours and did not require admission to traditional inpatient services. Provisional diagnoses for these 172 patients included very‐low risk possible ACS (n = 106), higher‐than‐very‐low risk possible ACS (n = 20), noncardiovascular diagnoses (n = 19), heart failure (n = 13), and other cardiovascular diagnoses (n = 14).

All patients73842 (22‐63)159*21
Possible ACS41837 (20‐57)131020
Heart failure21147 (34‐69)21927
Other CV6140 (21‐49)10511
Non‐CV4840 (22‐60)10213
P value <0.0010.040.180.01
Open access test     
Yes32046 (31‐67)17923
No41833 (20‐52)13920
P value <0.0010.150.870.43
Limited access test     
Yes12851 (42‐82)31633
No61038 (21‐55)121019
P value <0.001<0.0010.24<0.001
Acute care treatment     
Yes28746 (34‐69)211229
No45132 (20‐50)11716
P value <0.001<0.0010.01<0.001
Specialty consultation     
Yes14163 (40‐92)382852
No59738 (21‐50)10514
P value <0.001<0.001<0.001<0.001
Any above service     
Yes55446 (29‐68)191026
No18422 (16‐32)257
P value <0.001<0.0010.03<0.001
Factors Associated with Stays Longer than 72 Hours or Admissions to Traditional Inpatient Services: Multivariable Models
 Stay Longer than 72 HoursAdmission to Traditional Inpatient ServiceEither Outcome
OR*P valueOR*P valueOR*P value
  • NOTE: Models include all enrolled patients who did not leave prematurely against medical advice (n = 738).

  • Abbreviation: OR, odds ratio; SSU, short‐stay unit.

  • All ORs are adjusted for age, insulin‐requiring diabetes mellitus, whether or not patients were hospitalized during the last year, SSU attending physician, day of the week of SSU admission, and all other variables listed. For dichotomous variables, the OR represents a ratio of the odds for the group with the specified characteristic versus the odds for the group without that characteristic.

  • P values are Pearson's chi‐square test.

Heart failure2.30.011.10.771.90.02
Service received      
Open access test1.50.101.00.891.20.32
Limited access test5.1<0.0010.40.032.5<0.001
Acute care treatment1.70.071.40.311.60.05
Specialty consultation6.1<0.00113.1<0.0018.1<0.001

Admission Characteristics and Services Received

A narrow range of provisional diagnoses were listed by ED attending physicians and 641 patients (85% of 751) were grouped as having possible ACS or heart failure (Figure 2). Patients with these diagnoses were later risk‐stratified and, when pooled across risk strata, only 14 patients (2% of 641) exceeded the suggested admission‐location criterion for the SSU of lower than high risk. Despite the array and frequency of diagnostic and treatment services that patients received, SSU physicians worked mostly independently, requesting specialty consultations for only 19% of patients (141/738; Table 2).

SSU Success

The median LOS for all patients was 42 hours (interquartile range [IRQ] 22‐63) and 156 patients (21% of 738) had unsuccessful SSU stays (Table 3). The most common reason for an unsuccessful stay was a stay longer than 72 hours (71% of 156). Among the 66 patients who required admission to traditional inpatient services, nearly one‐half (48%) were admitted expressly to receive treatments not available in the SSU after having a specialty consult.

Patients' provisional diagnoses were associated with unsuccessful stays in bivariate analyses (Table 3). In addition, when patients were grouped into 3 risk stratums (very‐low, low, and intermediate‐and‐high), unsuccessful stays increased with increasing risk. For example, in patients with possible ACS, the proportion of unsuccessful stays increased from 17% of 306 very‐low risk patients to 27% of 55 intermediate‐and‐high risk patients (P value for trend = 0.012. Similarly, in patients with heart failure, the proportion of unsuccessful stays increased from 25% of 181 very‐low risk patients to 100% of 3 intermediate‐and‐high risk patients (P value for trend = 0.004).

However, in multiple variable models that simultaneously included patients' characteristics upon admission with services received during their SSU stay, only the provisional diagnosis of heart failure was associated with unsuccessful stays (OR, 1.9; 95% CI, 1.12‐3.18); risk assessments for possible ACS (P = 0.29) and heart failure (P = 0.32) were unimportant predictors of unsuccessful stays (Table 4). On the other hand, whether or not patients received diagnostic tests, acute care treatments, or specialty consultations were important predictors. In particular, patients who received specialty consultations were much more likely to require admission to traditional inpatient services than those patients who did not (OR, 13.1; 95% CI, 6.9‐24.9) and had a 52% chance of having an unsuccessful stay (95% CI, 42‐61%; Figure 3). In addition, the accessibility of a diagnostic test was inversely proportional to the chance of having a long stay; patients who received an open access test had a 12% chance of a long stay (95% CI, 8‐16%) whereas those who received a limited access test had a 29% chance of a long stay (95% CI, 20‐39%). Receiving acute care treatments was also a significant, though less important, predictor of an unsuccessful stay (Table 4).

Figure 3
Predicted probabilities of unsuccessful SSU stays from type of service received while in the SSU. The multivariable regression models used to generate these probabilities are described in Table 3. These models were constructed using all enrolled patients who did not leave prematurely against medical advice (n = 738) and were adjusted for age, insulin‐requiring diabetes mellitus, hospitalization during the last year, SSU attending physician, day of the week of SSU admission, provisional diagnosis of heart failure, and services received. The probability represents a patient's likelihood of a long stay, an eventual admission to traditional inpatient services, or either outcome having received a listed service. The vertical capped lines represent 95% confidence intervals for the probability estimates. Abbreviations: Consult., specialty consultation; Limited Ac., limited access test; Open Ac., open access test; Treat., acute care treatment.

Discussion

We found that the types of services received by patients during their SSU stays were stronger predictors of long stays and eventual admissions to traditional inpatient services than patients' characteristics upon admission to the SSU. This suggests that SSUs should be focused toward matching patients' anticipated needs with readily accessible services. For example, in our SSU, which cares for over 2,250 patients annually, more than 1,200 patients will receive diagnostic tests in a given year. Among these patients, those who receive a limited access test will be more than twice as likely to have long stays than those who receive an open access test (Figure 3). Though our conclusions may not be applicable to other settings, this study is the most comprehensive description of patients admitted to a hospitalist‐run SSU. In addition, our study is the first to demonstrate that diagnostic and consultative services are the most important predictors of successful stays in SSUs. This promotes the practical strategy that hospitalists who staff SSUs should focus administratively toward gaining access to these services.

Very few of our SSU patients did not fulfill the suggested requirements of our admission location guidelines. For example, only 2% of 691 patients with either possible ACS or heart failure were high risk (Figure 2). Despite this, 21% of our patients had stays longer than 72 hours or were admitted to traditional inpatient services. The paradoxically high proportion of unsuccessful stays among mostly very‐low and low risk patients simply reflects how the clinical risk models that we used were not designed to predict unsuccessful stays. Moreover, as our multiple variable models suggest, improvements in the selection process of candidate SSU patients are more likely to come from an ability to incorporate assessments of what services patients will receive rather than from assessments of their clinical risk (Table 4). Therefore, the immediate plans of the accepting SSU physicians, the physicians who will determine what services patients eventually receive, should be incorporated in the admission‐location decision process.

Three of our findings highlight how input from accepting SSU physiciansconveyed to ED physicians before their final admission‐location decisions are mademay improve the SSU patient selection process. First, 23% of our patients were discharged home after brief stays with no advanced tests, specialty consultations, or acute care treatments (Table 3). Though some of these patients may have required inpatient services other than the ones we recorded, most were admitted with very‐low risk possible ACS; if they required overnight stays at all, many of them may have been better cared for in the ED observation unit (Figure 1). Second, 74% of the patients who required admission to traditional inpatient services were admitted for services not readily available to patients in the SSU (Table 3). Among these patients, nearly one‐third (21/66) received no advanced diagnostic tests in the SSU. This suggests that these patients should have been admitted directly to the general medical wards; doing so may have improved efficiency and quality of care by reducing unnecessary handoffs between physicians. Both types of patientsthose with minimal inpatient needs and those with more needs than the SSU can providehighlight how incorporating accepting SSU physicians' plans may improve the SSU patient selection process. After all, those best equipped to determine if the SSU will meet (or exceed) the needs of candidate patients are the SSU physicians themselves.

Third, we found that whether or not SSU physicians required assistance from specialists was the strongest predictor of unsuccessful stays: when an accepting physician determined that a patient should receive a specialty consultation, that patient's chance of having an unsuccessful stay was over 50% (Figure 3). Our study was not designed to determine how specialty consultations were associated with unsuccessful stays. We did not, for example, record whether or not hospitalists changed their diagnostic, treatment, or admission plans because of specialists' recommendations.12 Therefore, we cannot conclude that specialty consultations actually caused long stays or traditional admissions. Nevertheless, when our SSU physicians did not manage patients independent of specialty consultations, we observed a high likelihood of unsuccessful stays. Because accepting SSU physicians are the ones who will determine whether or not they need assistance from specialists, weighing their immediate plans for specialty consultations into the admission‐location decision process may improve the efficiency of SSUs. Others have recognized the importance of specialty consultations in SSUs by directly incorporating specialists as coattending physicians.13

Our study had several limitations. First, we studied mostly patients with cardiovascular diagnoses. Predictors of success in SSUs that admit patients with different diagnostic profiles may be different. In particular, SSUs that admit patients with a wide array of diagnoses may find that matching patients' needs with readily accessible services is impractical, because these needs may be too wide‐ranging. Second, our study design was observational. However, other than seasonal variations in admission patterns, there was little room for selection bias because we enrolled all consecutive admissions over the 4‐month study period, which gives us more confidence in our results. Third, our study did not record whether or not ED physicians knowingly overrode the suggested admission‐location guidelines because of limited bed availability. Yet, if shortages of beds on traditional inpatient services were driving patients who were otherwise candidates for the general medical wards in to the SSU, then we would have expected higher‐risk patients and greater needs for limited access tests. Finally, our descriptions of patients' needs were based on what diagnostic, consultative, and treatment services patients actually received; yet these needs did not include diagnostic tests that were ordered but never performed. However, any missed needs would bias our results toward no association with unsuccessful stays, because unsuccessful stays would generally increase while patients await needed services.

Future research could address these limitations through an experimental trial of traditional admissions versus admission to a hospitalist‐run SSU. And, because hospitals are complex systems of health care delivery where changes in one patient care unit often affect others in unanticipated ways,14 the impact of SSUs on other patient care units that are closely connected to SSUs, such as EDs and the general medical wards (Figure 1), should be simultaneously observed. For example, though our findings suggest that the accessibility of diagnostic tests should parallel ordering SSU physicians' needs for those tests, making all diagnostic tests open to SSU physicians may result in shortsightedly lengthening the stays of patients in other care units. Future research should also observe the decision‐making process of both the physicians who make admission‐location decisions (ED physicians) and those who determine the eventual plans for patients in the SSU (hospitalists). Accepting physicians from other patient care units have found improved outcomes of efficiency when they were involved in the complex process of deciding where to admit patients.15, 16 After an initial evaluation of a candidate SSU patient in the ED, a hospitalist who staffs both the general medical wards and the SSU would be uniquely well‐positioned to help an ED physician decide where a patient's needs would be best met. Although ED physicians will rightly be concerned that consulting SSU hospitalists may slow patient flow, hands‐on consultations of candidate SSU patients, who have a narrow range of diagnoses and low‐risk profiles, would likely be brief. In addition, because many SSUs are conveniently adjacent to EDs, the burden of communication may be minor.1 To address these questions, hospitalists who staff SSUs must continue the observed trend of working collaboratively with ED physicians.15, 17, 18

Acknowledgements

The authors thank Arthur T. Evans and Brendan M. Reilly for their insightful review of the manuscript. The authors also thank Zhaotai Cui for his assistance with statistical programming.

References
  1. Taylor DMcD,Bennett DM,Cameron PA.A paradigm shift in the nature of care provision in emergency departments.Emerg Med J.2004;21:681684.
  2. Brady W,Harrigan R,Chan T.Acute coronary syndromes. In: Marx J, Hockberger R, Walls R, eds.Rosen's emergency medicine. Concepts and clinical practice.6th ed.Edinburgh:Mosby Elsevier;2006:11541199.
  3. Darves B.Taking charge of observation units for better patient flow.Todays Hospitalist Mag.2007;5(7):1620.
  4. Barbado Ajo MJ,Jimeno Carruez A,Ostolaza Vázquez JM, et al.Unidad de corta estancia dependiente de Medicina Interna.An Med Interna.1999;16:504510.
  5. Diz‐Lois Palomares MT,de la Inglesia Martinez F,Nicolás Miguel R,Pellicer Vázquez C Ramos Polledo V,Diz‐Lois Martinez F.Factors that predict unplanned hospital readmission of patients discharged from a short stay medical unit.An Med Interna.2002;19:221225.
  6. Abenhaim HA,Kahn SR,Raffoul J, et al.Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital.CMAJ.2000;163(11):14771480.
  7. Reilly BM,Evans AT,Schaider JJ,Wang Y.Triage of patients with chest pain in the emergency department: a comparative study of physicians' decisions.Am J Med.2002;112:95103.
  8. Cook RI,Render M,Woods DD.Gaps in the continuity of care and progress on patient safety.BMJ.2000;320:791794.
  9. Goldman L,Cook EF,Johnson PA, et al.Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain.N Engl J Med.1996;334:14981504.
  10. Fonarow GC,Adams KF,Abraham WT, et al.Risk stratification for in‐hospital mortality in acutely decompensated heart failure.JAMA.2005;293:572580.
  11. Cuzick J.A Wilcoxon‐type test for trend.Stat Med.1985;4:8790.
  12. Braham RL,Ron A,Ruchlin HS,Hollenberg JP,Pompei P,Charlson ME.Diagnostic test restraint and the specialty consultation.J Gen Intern Med.1990;5:95103.
  13. Krantz MJ,Zwant O,Rowan SB, et al.A cooperative care model: cardiologists and hospitalists reduce length of stay in a chest pain observation unit.Crit Pathw Cardiol.2005;4:5558.
  14. Black D,Pearson M.Average length of stay, delayed discharge, and hospital congestion.BMJ.2002;325:610611.
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  16. Multz AS,Chalfin DB,Samson IM, et al.A “closed” medical intensive care unit (MICU) improves resource utilization when compared with an “open” MICU.Am J Respir Crit Care Med.1998;157:14681473.
  17. Darves B.Hospitalists' new role in the ED: “clog busters.”Todays Hospitalist Mag.2005;3(8):1518.
  18. Sattinger A.Kindred spirits: ED doctors, hospitalists forge a critical collaboration.Hospitalist.2007;11(7):1,16–20.
Article PDF
Issue
Journal of Hospital Medicine - 4(5)
Page Number
276-284
Legacy Keywords
consultation, health facility environment, hospital units, hospitalists
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Article PDF
Article PDF

Short‐stay units (SSUs) are common alternatives to traditional inpatient services.1 When defined broadly to include observation units for low‐risk chest pain patients, SSUs exist in one‐third of hospitals in the United States.2 Amidst growing demands for inpatient services, SSUs have recently developed beyond observation medicine to provide more complex inpatient services in locations commonly adjacent to emergency departments (EDs).1 Hospitalists are well‐positioned to staff these emerging SSUs because of their expertise in managing complex inpatient services.3

Despite this, we found only 3 reports of hospitalist‐run SSUs designed for general medical inpatients (2 from Spain and 1 from Canada).46 Whereas these early reports introduce hospitalist‐run SSUs, they provide limited data to make firm conclusions about their usefulness or appropriate design. For example, none of these reports assessed patients' characteristics upon admission. Nor did they provide details about the services that the SSUs provided. Yet evaluation of both types of patient‐level datadescriptions of patients' needs upon admission and how these needs are met during their staysdetermine whether or not hospitalist‐run SSUs meet their potential to efficiently care for backlogs of patients who otherwise await admission to traditional inpatient services.

In order to further explore these issues, we first sought to characterize our SSU patients upon admission and record what services they received during their stays. To help interpret our results, we then investigated associations between these characteristics and measures of successfully caring for patients in our SSU.

Patients and Methods

Design and Setting

In this prospective cohort study, we included all patients admitted to the hospitalist‐run SSU of Cook County Hospital, a 500‐bed public teaching hospital in Chicago, Illinois, from January through April of 2006. Our 14‐bed SSU opened in 2002 to reduce overcrowding on the traditional inpatient wards by admitting adult patients who require inpatient care but might be eligible for discharge within 3 days. The unit is geographically part of the ED but is staffed by resident physicians and a rotating group of hospitalist attending physicians from the Department of Medicine. At least 1 attending and resident physician are available throughout the day, including weekend days and holidays; evenings are covered by a resident who presents overnight admissions to an attending physician the following morning.

ED physicians admit general medical patients to the SSU 24 hours per day, 7 days per week. Though admissions do not require prior approval from SSU physicians, the Departments of Medicine and Emergency Medicine have collaboratively promoted 5 suggested admission‐location guidelines to admitting ED physicians (Figure 1). For candidate SSU patients, these 5 guidelines are not intended to be restrictive but to provide a framework for the complex decision‐making process that our ED physicians encounter, particularly during periods of extreme overcrowding.7 First, patients should have an anticipated stay shorter than 72 hours. Second, patients should not have an eventual need for admission to traditional inpatient services such as the general medicine wards or intensive care units; this guideline is intended to improve patient safety by reducing unnecessary handoffs between physicians.8 Third, patients with provisional cardiovascular diagnoses should be preferentially admitted to the SSU over the general medical wards; this guideline is intended to improve hospital‐wide efficiency because our SSU is equipped with continuous telemetry monitors, an exercise treadmill testing (ETT) laboratory, and other reserved cardiac tests (see Admission Characteristics and Services Received section, below). Fourth, patients' risk should be no higher than intermediate level. Admitting ED physicians are encouraged to use posted risk estimators for patients with provisional diagnoses of possible acute coronary syndrome (ACS), decompensated heart failure, asthma exacerbation, and out‐of‐control diabetes. Finally, patients should not need advanced ancillary services; these include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements).

Figure 1
Flow diagram and suggested admission‐location guidelines for general medicine patients who require overnight hospital stays. Flow begins at the base of the figure, which represents the major point‐of‐entry for 90% of our patients in to the hospital: the emergency department. Widths of arrows are approximately proportional to the flow of patients from the ED to other patient care units. aAdvanced ancillary services include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements). Abbreviations: ACS, acute coronary syndrome; ED, emergency department; LOS, length‐of‐stay; PAs, physician assistants.

Subjects

The study subjects were all patients admitted to the SSU during the 4‐month study period. Patients were excluded from the entire study if they refused verbal consent to participate. All patients who consented were included in the description of patient admission characteristics. Thirteen patients who prematurely left the SSU against medical advice, however, were neither included in the descriptions of services received nor in the analyses of predictors of successful SSU stays. We excluded these patients because they needed services that they did not receiveincluding these patients in our analysis would tend to overestimate the efficiency of our SSU by shortening the length‐of‐stay (LOS) without adding diagnostic tests or treatments.

Data Collection

After receiving approval from the institutional review board, attending physician investigators conducted an interview, physical examination, and review of medical records for each enrolled patient within 12 hours of admission to the SSU. When ED attending physicians' provisional primary diagnoses included possible ACS or decompensated heart failure, which we knew from earlier pilot data were our 2 most common provisional diagnoses, investigators gathered patient data to be applied in validated models of risk after the study period (Figure 2).9, 10 Some of the clinical predictors required for these models are based on patients' findings on presentation to the ED. For example, Goldman's risk model for major cardiac events uses patients' initial systolic blood pressures on presentation to the ED.9 In such cases, investigators gathered needed data from electronic and paper charts generated in the ED. Upon discharge from the SSU, investigators reviewed patients' medical records a second time. All data were entered by investigators and instantly committed into an online database.

Figure 2
Provisional diagnostic groupings and risk assessments upon admission to the SSU during the 4‐month study period. aNoncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis. bThe validated risk model of Goldman et al.9 predicts major cardiac events in 72 hours. Major cardiac events include ventricular fibrillation, cardiac arrest, new complete heart block, insertion of a temporary pacemaker, emergent cardioversion, cardiogenic shock, use of an intraaortic balloon pump, endotracheal intubation, and recurrent ischemic chest pain requiring urgent coronary angiography and urgent revascularization. cThe validated risk model of Fonarow et al.10 predicts in‐hospital mortality rate. The original model classified patients into 5 risk stratums. We modified the model by combining their lowest intermediate risk stratums (“intermediate 3” and “intermediate 2”), which had similar crude mortality rates in their validation cohorts, in to a single “low‐risk” stratum. dOther cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease. eThe 14 patients with possible ACS who were high risk all had electrocardiographic findings that were both not known to be old and were suggestive of ST‐segment elevation myocardial infarction. Yet upon admission to the SSU, all of these 14 patients had 2 negative serum troponin I tests that were drawn 8 hours apart (data not shown), suggesting that their electrocardiographic findings were in fact old. Abbreviation: ACS, acute coronary syndrome.

Admission Characteristics and Services Received

Patients were grouped according to the provisional diagnoses of ED attending physicians upon admission to the SSU (Figure 2). We chose to group patients by the provisional diagnoses of EDnot SSUattending physicians to better understand how ED physicians, the physicians who make the admission‐location decisions in our hospital, were using the SSU. Patients were first grouped as having possible ACS or heart failure, because patients with these provisional diagnoses were preferentially admitted to the SSU (Figure 1). When neither diagnosis was listed, patients were grouped according to ED attending physicians' first‐listed diagnoses. At the end of the study period, relevant risk models were applied to patients with possible ACS or heart failure and stratified as very low, low, intermediate, or high risk.9, 10 Patients with both possible ACS and heart failure were grouped according to the diagnosis with the highest corresponding risk assessment; if both risk assessments were the same, then the first‐listed diagnosis was used. Though developed to predict different clinical outcomes during different time periods, risk strata from the corresponding risk models were pooled across both diagnoses to develop a risk summary.

Upon discharge, investigators recorded which advanced diagnostic tests, specialty consultations, and acute care treatments patients received while in the SSU. Diagnostic tests were considered advanced if they were not routinely performed within 2 hours of being ordered. Advanced diagnostic tests were grouped into 2 types by their accessibility to ordering SSU physicians. Open access tests included echocardiograms and ETTs, which were reserved for SSU patients 6 days per week. Though the availability of open access tests was not unlimited, ordering physicians' needs for them rarely exceeded the immediate supply. On the other hand, limited access tests included both cardiac stress imaging studies, which were reserved for SSU patients on a very limited basis 4 or 5 days per week, and other tests that were not reserved for SSU patients, such as endoscopy, magnetic resonance imaging, or ultrasonography. Ordering physicians' needs for limited access tests often exceeded their immediate supply; in such cases, SSU patients were placed without priority into queues that included patients from the entire hospital.

Investigators recorded when patients received advanced diagnostic tests that were ordered by specialists. These tests, however, were not included in analyses of how services received by SSU patients affected SSU success, because SSU attending physicians were only indirectly involved in whether or not patients received these tests. Treatments were considered acute care treatments if they were commonly administered only in acute care settings, such as heparin for unstable angina or intravenous furosemide for pulmonary edema.

SSU Success

The SSU was designed to care for patients during brief stays and without eventual admission to traditional inpatient services. Therefore, we used patients' LOS and whether or not patients were admitted to traditional inpatient services as measures of SSU success. LOS was calculated from the time patients arrived in the SSU until the time they left. Therefore, neither time spent in the ED before admission to the SSU nor time spent on traditional inpatient services (if needed) contributed to our definition of LOS. Individual SSU patients were considered successfully cared for in the SSU if their LOS was less than 72 hours and they were discharged directly home from the SSU. We explored associations between these outcomes and provisional diagnoses, risk assessments, and services received.

Data Analysis

LOS data were right‐skewed; therefore, we used the Mann‐Whitney test for comparisons between 2 groups and the Kruskal‐Wallis test for comparisons among 3 or more groups. To test for trends of median LOS among ordered groupings, we used the method of Cuzick.11 We used Pearson's chi‐square test to compare proportions of patients grouped into categories and the chi‐square test for trends with equal scoring to test for trends among ordered groupings.

We performed multiple logistic regression to explore which variables were associated with SSU success. The following 5 demographic variables from Table 1 were insignificant in all single‐variable and multiple‐variable regression models that we tested and were, therefore, removed from further analyses to create more parsimonious models: gender, language, ethnicity, race, and whether or not patients had a primary care provider. Our multiple logistic regression models were fitted by maximum likelihood methods. In all of these models, odds ratios (ORs) were adjusted for patient characteristics that included age (in years), insulin‐requiring diabetes mellitus (yes or no), SSU attending physician, day of the week of SSU admission (weekday or weekend), and hospitalization during the preceding year. Confidence intervals (CIs) for predicted probabilities were computed using the delta method. All analyses were conducted with Stata Statistical Software, Release 9 (StataCorp, College Station, TX).

Admission Characteristics of Enrolled Short‐Stay Unit Patients
  • NOTE: n = 751. Values are n (%) unless otherwise indicated.

  • Emergency department physicians listed an additional provisional diagnosis for 186 patients (25% of 751).

  • Other cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease.

  • Noncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis.

Mean age, years (SD) (25th‐75th percentiles)58 (12) (49‐66)
Men389 (52)
Lacking a primary care provider256 (34)
Non‐English speaking217 (29)
Ethnicity is Hispanic or Latino105 (14)
Race is Black or African‐American480 (64)
Hospitalized within the preceding year322 (43)
Insulin‐requiring diabetes mellitus83 (11)
Previous coronary artery revascularization89 (12)
Provisional diagnosis* 
Possible acute coronary syndrome427 (57)
Heart failure214 (29)
Other cardiovascular62 (8)
Noncardiovascular48 (6)

Results

Subjects

During the 4‐month study period, 755 patients were admitted to the SSU. Among these patients, 4 were excluded from our study because they refused verbal consent. In the remaining study sample of 751 patients, all were included in the descriptions of patients' admission characteristics (Table 1), but 13 patients who left prematurely were excluded in both the descriptions of services received (Table 2) and the analyses of SSU success (Tables 3 and 4).

Services Received by Provisional Diagnosis
Service receivedPossible ACS n = 418 (%)Heart Failure n = 211 (%)Other Cardiovascular n = 61 (%)Noncardiovascular n = 48 (%)Total n = 738 (%)
  • NOTE: n = 738. Does not include 13 patients who prematurely left the SSU against medical advice. P values for Pearson's chi‐square test for differences in proportions across all four groups were all <0.001.

  • Abbreviations: ACS, acute coronary syndrome; ETT, exercise treadmill test.

  • Eighteen patients received both a resting echocardiogram and an ETT.

  • Two patients received both a stress imaging test and another limited access test. Other limited access tests included esophagogastroduodenoscopy, colonoscopy, brain or spine magnetic resonance imaging, abdominal ultrasonography, carotid artery ultrasonography, cardiac multiple gated acquisition scan, bone scintigraphy, cardiac pacemaker interrogation, pulmonary angiography, and ventilation‐perfusion scan; 6 patients received 2 such tests.

  • Stress imaging tests included myocardial perfusion imaging and stress echocardiography; 3 patients received 2 stress imaging studies.

  • Acute care treatments included intravenous furosemide, nebulized albuterol or ipratropium, treatment doses of heparin, intravenous antibiotics, and intravenous insulin; 10 patients received 2 or more acute care treatments.

  • Additional diagnostic tests that required arrangement by specialty consultants were not considered open or limited access tests. They included coronary angiography, transesophageal echocardiography, cardiac electrophysiology study, and electroencephalography.

Open access test*3759561343
Resting     
Echocardiography2959561339
ETT120507
Limited access test24810817
Stress imaging2157214
Acute care treatment227856039
Specialty consultation241220819
Any above service6893676975
Patient Outcome by Provisional Diagnosis and Services Received
Provisional Diagnosis and Services ReceivednMedian LOS [hours (IQR)]Stay Longer than 72 Hours (%)Admission to Traditional Inpatient Service (%)Stay Longer than 72 Hours or Admission to Traditional Inpatient Service (%)
  • NOTE: n = 738. Does not include 13 patients who left prematurely against medical advice. P values are for Pearson's chi‐square test unless otherwise indicated.

  • Abbreviations: ACS, acute coronary syndrome; CV, cardiovascular; IQR, interquartile range; LOS, length‐of‐stay.

  • Among the 9% of patients (66/738) who were admitted to the traditional inpatient services, SSU attending physicians' reasons for admission for 74% of patients (49/66) were to provide treatments (n = 32), diagnostic tests (n = 14), and ancillary services (n = 3) not provided in the SSU or because prolonged treatment courses were anticipated on the current therapies (n = 17). Prior to admission to traditional inpatient services, 21 patients (32% of 66) had received no advanced diagnostic tests, and 9 patients (14% of 66) had received no advanced diagnostic tests, acute care treatments, or specialty consultations.

  • P values are for differences among provisional diagnostic groupings.

  • Nonparametric Kruskal‐Wallis test.

  • Nonparametric Mann‐Whitney test.

  • Among the 184 patients who did not receive any of the above services, 172 (23% of 738) stayed less than 72 hours and did not require admission to traditional inpatient services. Provisional diagnoses for these 172 patients included very‐low risk possible ACS (n = 106), higher‐than‐very‐low risk possible ACS (n = 20), noncardiovascular diagnoses (n = 19), heart failure (n = 13), and other cardiovascular diagnoses (n = 14).

All patients73842 (22‐63)159*21
Possible ACS41837 (20‐57)131020
Heart failure21147 (34‐69)21927
Other CV6140 (21‐49)10511
Non‐CV4840 (22‐60)10213
P value <0.0010.040.180.01
Open access test     
Yes32046 (31‐67)17923
No41833 (20‐52)13920
P value <0.0010.150.870.43
Limited access test     
Yes12851 (42‐82)31633
No61038 (21‐55)121019
P value <0.001<0.0010.24<0.001
Acute care treatment     
Yes28746 (34‐69)211229
No45132 (20‐50)11716
P value <0.001<0.0010.01<0.001
Specialty consultation     
Yes14163 (40‐92)382852
No59738 (21‐50)10514
P value <0.001<0.001<0.001<0.001
Any above service     
Yes55446 (29‐68)191026
No18422 (16‐32)257
P value <0.001<0.0010.03<0.001
Factors Associated with Stays Longer than 72 Hours or Admissions to Traditional Inpatient Services: Multivariable Models
 Stay Longer than 72 HoursAdmission to Traditional Inpatient ServiceEither Outcome
OR*P valueOR*P valueOR*P value
  • NOTE: Models include all enrolled patients who did not leave prematurely against medical advice (n = 738).

  • Abbreviation: OR, odds ratio; SSU, short‐stay unit.

  • All ORs are adjusted for age, insulin‐requiring diabetes mellitus, whether or not patients were hospitalized during the last year, SSU attending physician, day of the week of SSU admission, and all other variables listed. For dichotomous variables, the OR represents a ratio of the odds for the group with the specified characteristic versus the odds for the group without that characteristic.

  • P values are Pearson's chi‐square test.

Heart failure2.30.011.10.771.90.02
Service received      
Open access test1.50.101.00.891.20.32
Limited access test5.1<0.0010.40.032.5<0.001
Acute care treatment1.70.071.40.311.60.05
Specialty consultation6.1<0.00113.1<0.0018.1<0.001

Admission Characteristics and Services Received

A narrow range of provisional diagnoses were listed by ED attending physicians and 641 patients (85% of 751) were grouped as having possible ACS or heart failure (Figure 2). Patients with these diagnoses were later risk‐stratified and, when pooled across risk strata, only 14 patients (2% of 641) exceeded the suggested admission‐location criterion for the SSU of lower than high risk. Despite the array and frequency of diagnostic and treatment services that patients received, SSU physicians worked mostly independently, requesting specialty consultations for only 19% of patients (141/738; Table 2).

SSU Success

The median LOS for all patients was 42 hours (interquartile range [IRQ] 22‐63) and 156 patients (21% of 738) had unsuccessful SSU stays (Table 3). The most common reason for an unsuccessful stay was a stay longer than 72 hours (71% of 156). Among the 66 patients who required admission to traditional inpatient services, nearly one‐half (48%) were admitted expressly to receive treatments not available in the SSU after having a specialty consult.

Patients' provisional diagnoses were associated with unsuccessful stays in bivariate analyses (Table 3). In addition, when patients were grouped into 3 risk stratums (very‐low, low, and intermediate‐and‐high), unsuccessful stays increased with increasing risk. For example, in patients with possible ACS, the proportion of unsuccessful stays increased from 17% of 306 very‐low risk patients to 27% of 55 intermediate‐and‐high risk patients (P value for trend = 0.012. Similarly, in patients with heart failure, the proportion of unsuccessful stays increased from 25% of 181 very‐low risk patients to 100% of 3 intermediate‐and‐high risk patients (P value for trend = 0.004).

However, in multiple variable models that simultaneously included patients' characteristics upon admission with services received during their SSU stay, only the provisional diagnosis of heart failure was associated with unsuccessful stays (OR, 1.9; 95% CI, 1.12‐3.18); risk assessments for possible ACS (P = 0.29) and heart failure (P = 0.32) were unimportant predictors of unsuccessful stays (Table 4). On the other hand, whether or not patients received diagnostic tests, acute care treatments, or specialty consultations were important predictors. In particular, patients who received specialty consultations were much more likely to require admission to traditional inpatient services than those patients who did not (OR, 13.1; 95% CI, 6.9‐24.9) and had a 52% chance of having an unsuccessful stay (95% CI, 42‐61%; Figure 3). In addition, the accessibility of a diagnostic test was inversely proportional to the chance of having a long stay; patients who received an open access test had a 12% chance of a long stay (95% CI, 8‐16%) whereas those who received a limited access test had a 29% chance of a long stay (95% CI, 20‐39%). Receiving acute care treatments was also a significant, though less important, predictor of an unsuccessful stay (Table 4).

Figure 3
Predicted probabilities of unsuccessful SSU stays from type of service received while in the SSU. The multivariable regression models used to generate these probabilities are described in Table 3. These models were constructed using all enrolled patients who did not leave prematurely against medical advice (n = 738) and were adjusted for age, insulin‐requiring diabetes mellitus, hospitalization during the last year, SSU attending physician, day of the week of SSU admission, provisional diagnosis of heart failure, and services received. The probability represents a patient's likelihood of a long stay, an eventual admission to traditional inpatient services, or either outcome having received a listed service. The vertical capped lines represent 95% confidence intervals for the probability estimates. Abbreviations: Consult., specialty consultation; Limited Ac., limited access test; Open Ac., open access test; Treat., acute care treatment.

Discussion

We found that the types of services received by patients during their SSU stays were stronger predictors of long stays and eventual admissions to traditional inpatient services than patients' characteristics upon admission to the SSU. This suggests that SSUs should be focused toward matching patients' anticipated needs with readily accessible services. For example, in our SSU, which cares for over 2,250 patients annually, more than 1,200 patients will receive diagnostic tests in a given year. Among these patients, those who receive a limited access test will be more than twice as likely to have long stays than those who receive an open access test (Figure 3). Though our conclusions may not be applicable to other settings, this study is the most comprehensive description of patients admitted to a hospitalist‐run SSU. In addition, our study is the first to demonstrate that diagnostic and consultative services are the most important predictors of successful stays in SSUs. This promotes the practical strategy that hospitalists who staff SSUs should focus administratively toward gaining access to these services.

Very few of our SSU patients did not fulfill the suggested requirements of our admission location guidelines. For example, only 2% of 691 patients with either possible ACS or heart failure were high risk (Figure 2). Despite this, 21% of our patients had stays longer than 72 hours or were admitted to traditional inpatient services. The paradoxically high proportion of unsuccessful stays among mostly very‐low and low risk patients simply reflects how the clinical risk models that we used were not designed to predict unsuccessful stays. Moreover, as our multiple variable models suggest, improvements in the selection process of candidate SSU patients are more likely to come from an ability to incorporate assessments of what services patients will receive rather than from assessments of their clinical risk (Table 4). Therefore, the immediate plans of the accepting SSU physicians, the physicians who will determine what services patients eventually receive, should be incorporated in the admission‐location decision process.

Three of our findings highlight how input from accepting SSU physiciansconveyed to ED physicians before their final admission‐location decisions are mademay improve the SSU patient selection process. First, 23% of our patients were discharged home after brief stays with no advanced tests, specialty consultations, or acute care treatments (Table 3). Though some of these patients may have required inpatient services other than the ones we recorded, most were admitted with very‐low risk possible ACS; if they required overnight stays at all, many of them may have been better cared for in the ED observation unit (Figure 1). Second, 74% of the patients who required admission to traditional inpatient services were admitted for services not readily available to patients in the SSU (Table 3). Among these patients, nearly one‐third (21/66) received no advanced diagnostic tests in the SSU. This suggests that these patients should have been admitted directly to the general medical wards; doing so may have improved efficiency and quality of care by reducing unnecessary handoffs between physicians. Both types of patientsthose with minimal inpatient needs and those with more needs than the SSU can providehighlight how incorporating accepting SSU physicians' plans may improve the SSU patient selection process. After all, those best equipped to determine if the SSU will meet (or exceed) the needs of candidate patients are the SSU physicians themselves.

Third, we found that whether or not SSU physicians required assistance from specialists was the strongest predictor of unsuccessful stays: when an accepting physician determined that a patient should receive a specialty consultation, that patient's chance of having an unsuccessful stay was over 50% (Figure 3). Our study was not designed to determine how specialty consultations were associated with unsuccessful stays. We did not, for example, record whether or not hospitalists changed their diagnostic, treatment, or admission plans because of specialists' recommendations.12 Therefore, we cannot conclude that specialty consultations actually caused long stays or traditional admissions. Nevertheless, when our SSU physicians did not manage patients independent of specialty consultations, we observed a high likelihood of unsuccessful stays. Because accepting SSU physicians are the ones who will determine whether or not they need assistance from specialists, weighing their immediate plans for specialty consultations into the admission‐location decision process may improve the efficiency of SSUs. Others have recognized the importance of specialty consultations in SSUs by directly incorporating specialists as coattending physicians.13

Our study had several limitations. First, we studied mostly patients with cardiovascular diagnoses. Predictors of success in SSUs that admit patients with different diagnostic profiles may be different. In particular, SSUs that admit patients with a wide array of diagnoses may find that matching patients' needs with readily accessible services is impractical, because these needs may be too wide‐ranging. Second, our study design was observational. However, other than seasonal variations in admission patterns, there was little room for selection bias because we enrolled all consecutive admissions over the 4‐month study period, which gives us more confidence in our results. Third, our study did not record whether or not ED physicians knowingly overrode the suggested admission‐location guidelines because of limited bed availability. Yet, if shortages of beds on traditional inpatient services were driving patients who were otherwise candidates for the general medical wards in to the SSU, then we would have expected higher‐risk patients and greater needs for limited access tests. Finally, our descriptions of patients' needs were based on what diagnostic, consultative, and treatment services patients actually received; yet these needs did not include diagnostic tests that were ordered but never performed. However, any missed needs would bias our results toward no association with unsuccessful stays, because unsuccessful stays would generally increase while patients await needed services.

Future research could address these limitations through an experimental trial of traditional admissions versus admission to a hospitalist‐run SSU. And, because hospitals are complex systems of health care delivery where changes in one patient care unit often affect others in unanticipated ways,14 the impact of SSUs on other patient care units that are closely connected to SSUs, such as EDs and the general medical wards (Figure 1), should be simultaneously observed. For example, though our findings suggest that the accessibility of diagnostic tests should parallel ordering SSU physicians' needs for those tests, making all diagnostic tests open to SSU physicians may result in shortsightedly lengthening the stays of patients in other care units. Future research should also observe the decision‐making process of both the physicians who make admission‐location decisions (ED physicians) and those who determine the eventual plans for patients in the SSU (hospitalists). Accepting physicians from other patient care units have found improved outcomes of efficiency when they were involved in the complex process of deciding where to admit patients.15, 16 After an initial evaluation of a candidate SSU patient in the ED, a hospitalist who staffs both the general medical wards and the SSU would be uniquely well‐positioned to help an ED physician decide where a patient's needs would be best met. Although ED physicians will rightly be concerned that consulting SSU hospitalists may slow patient flow, hands‐on consultations of candidate SSU patients, who have a narrow range of diagnoses and low‐risk profiles, would likely be brief. In addition, because many SSUs are conveniently adjacent to EDs, the burden of communication may be minor.1 To address these questions, hospitalists who staff SSUs must continue the observed trend of working collaboratively with ED physicians.15, 17, 18

Acknowledgements

The authors thank Arthur T. Evans and Brendan M. Reilly for their insightful review of the manuscript. The authors also thank Zhaotai Cui for his assistance with statistical programming.

Short‐stay units (SSUs) are common alternatives to traditional inpatient services.1 When defined broadly to include observation units for low‐risk chest pain patients, SSUs exist in one‐third of hospitals in the United States.2 Amidst growing demands for inpatient services, SSUs have recently developed beyond observation medicine to provide more complex inpatient services in locations commonly adjacent to emergency departments (EDs).1 Hospitalists are well‐positioned to staff these emerging SSUs because of their expertise in managing complex inpatient services.3

Despite this, we found only 3 reports of hospitalist‐run SSUs designed for general medical inpatients (2 from Spain and 1 from Canada).46 Whereas these early reports introduce hospitalist‐run SSUs, they provide limited data to make firm conclusions about their usefulness or appropriate design. For example, none of these reports assessed patients' characteristics upon admission. Nor did they provide details about the services that the SSUs provided. Yet evaluation of both types of patient‐level datadescriptions of patients' needs upon admission and how these needs are met during their staysdetermine whether or not hospitalist‐run SSUs meet their potential to efficiently care for backlogs of patients who otherwise await admission to traditional inpatient services.

In order to further explore these issues, we first sought to characterize our SSU patients upon admission and record what services they received during their stays. To help interpret our results, we then investigated associations between these characteristics and measures of successfully caring for patients in our SSU.

Patients and Methods

Design and Setting

In this prospective cohort study, we included all patients admitted to the hospitalist‐run SSU of Cook County Hospital, a 500‐bed public teaching hospital in Chicago, Illinois, from January through April of 2006. Our 14‐bed SSU opened in 2002 to reduce overcrowding on the traditional inpatient wards by admitting adult patients who require inpatient care but might be eligible for discharge within 3 days. The unit is geographically part of the ED but is staffed by resident physicians and a rotating group of hospitalist attending physicians from the Department of Medicine. At least 1 attending and resident physician are available throughout the day, including weekend days and holidays; evenings are covered by a resident who presents overnight admissions to an attending physician the following morning.

ED physicians admit general medical patients to the SSU 24 hours per day, 7 days per week. Though admissions do not require prior approval from SSU physicians, the Departments of Medicine and Emergency Medicine have collaboratively promoted 5 suggested admission‐location guidelines to admitting ED physicians (Figure 1). For candidate SSU patients, these 5 guidelines are not intended to be restrictive but to provide a framework for the complex decision‐making process that our ED physicians encounter, particularly during periods of extreme overcrowding.7 First, patients should have an anticipated stay shorter than 72 hours. Second, patients should not have an eventual need for admission to traditional inpatient services such as the general medicine wards or intensive care units; this guideline is intended to improve patient safety by reducing unnecessary handoffs between physicians.8 Third, patients with provisional cardiovascular diagnoses should be preferentially admitted to the SSU over the general medical wards; this guideline is intended to improve hospital‐wide efficiency because our SSU is equipped with continuous telemetry monitors, an exercise treadmill testing (ETT) laboratory, and other reserved cardiac tests (see Admission Characteristics and Services Received section, below). Fourth, patients' risk should be no higher than intermediate level. Admitting ED physicians are encouraged to use posted risk estimators for patients with provisional diagnoses of possible acute coronary syndrome (ACS), decompensated heart failure, asthma exacerbation, and out‐of‐control diabetes. Finally, patients should not need advanced ancillary services; these include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements).

Figure 1
Flow diagram and suggested admission‐location guidelines for general medicine patients who require overnight hospital stays. Flow begins at the base of the figure, which represents the major point‐of‐entry for 90% of our patients in to the hospital: the emergency department. Widths of arrows are approximately proportional to the flow of patients from the ED to other patient care units. aAdvanced ancillary services include bedside procedures (eg, central venous catheter insertions), time‐intensive nursing (eg, regular dressing changes), and complex social‐services (eg, long‐term care facility placements). Abbreviations: ACS, acute coronary syndrome; ED, emergency department; LOS, length‐of‐stay; PAs, physician assistants.

Subjects

The study subjects were all patients admitted to the SSU during the 4‐month study period. Patients were excluded from the entire study if they refused verbal consent to participate. All patients who consented were included in the description of patient admission characteristics. Thirteen patients who prematurely left the SSU against medical advice, however, were neither included in the descriptions of services received nor in the analyses of predictors of successful SSU stays. We excluded these patients because they needed services that they did not receiveincluding these patients in our analysis would tend to overestimate the efficiency of our SSU by shortening the length‐of‐stay (LOS) without adding diagnostic tests or treatments.

Data Collection

After receiving approval from the institutional review board, attending physician investigators conducted an interview, physical examination, and review of medical records for each enrolled patient within 12 hours of admission to the SSU. When ED attending physicians' provisional primary diagnoses included possible ACS or decompensated heart failure, which we knew from earlier pilot data were our 2 most common provisional diagnoses, investigators gathered patient data to be applied in validated models of risk after the study period (Figure 2).9, 10 Some of the clinical predictors required for these models are based on patients' findings on presentation to the ED. For example, Goldman's risk model for major cardiac events uses patients' initial systolic blood pressures on presentation to the ED.9 In such cases, investigators gathered needed data from electronic and paper charts generated in the ED. Upon discharge from the SSU, investigators reviewed patients' medical records a second time. All data were entered by investigators and instantly committed into an online database.

Figure 2
Provisional diagnostic groupings and risk assessments upon admission to the SSU during the 4‐month study period. aNoncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis. bThe validated risk model of Goldman et al.9 predicts major cardiac events in 72 hours. Major cardiac events include ventricular fibrillation, cardiac arrest, new complete heart block, insertion of a temporary pacemaker, emergent cardioversion, cardiogenic shock, use of an intraaortic balloon pump, endotracheal intubation, and recurrent ischemic chest pain requiring urgent coronary angiography and urgent revascularization. cThe validated risk model of Fonarow et al.10 predicts in‐hospital mortality rate. The original model classified patients into 5 risk stratums. We modified the model by combining their lowest intermediate risk stratums (“intermediate 3” and “intermediate 2”), which had similar crude mortality rates in their validation cohorts, in to a single “low‐risk” stratum. dOther cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease. eThe 14 patients with possible ACS who were high risk all had electrocardiographic findings that were both not known to be old and were suggestive of ST‐segment elevation myocardial infarction. Yet upon admission to the SSU, all of these 14 patients had 2 negative serum troponin I tests that were drawn 8 hours apart (data not shown), suggesting that their electrocardiographic findings were in fact old. Abbreviation: ACS, acute coronary syndrome.

Admission Characteristics and Services Received

Patients were grouped according to the provisional diagnoses of ED attending physicians upon admission to the SSU (Figure 2). We chose to group patients by the provisional diagnoses of EDnot SSUattending physicians to better understand how ED physicians, the physicians who make the admission‐location decisions in our hospital, were using the SSU. Patients were first grouped as having possible ACS or heart failure, because patients with these provisional diagnoses were preferentially admitted to the SSU (Figure 1). When neither diagnosis was listed, patients were grouped according to ED attending physicians' first‐listed diagnoses. At the end of the study period, relevant risk models were applied to patients with possible ACS or heart failure and stratified as very low, low, intermediate, or high risk.9, 10 Patients with both possible ACS and heart failure were grouped according to the diagnosis with the highest corresponding risk assessment; if both risk assessments were the same, then the first‐listed diagnosis was used. Though developed to predict different clinical outcomes during different time periods, risk strata from the corresponding risk models were pooled across both diagnoses to develop a risk summary.

Upon discharge, investigators recorded which advanced diagnostic tests, specialty consultations, and acute care treatments patients received while in the SSU. Diagnostic tests were considered advanced if they were not routinely performed within 2 hours of being ordered. Advanced diagnostic tests were grouped into 2 types by their accessibility to ordering SSU physicians. Open access tests included echocardiograms and ETTs, which were reserved for SSU patients 6 days per week. Though the availability of open access tests was not unlimited, ordering physicians' needs for them rarely exceeded the immediate supply. On the other hand, limited access tests included both cardiac stress imaging studies, which were reserved for SSU patients on a very limited basis 4 or 5 days per week, and other tests that were not reserved for SSU patients, such as endoscopy, magnetic resonance imaging, or ultrasonography. Ordering physicians' needs for limited access tests often exceeded their immediate supply; in such cases, SSU patients were placed without priority into queues that included patients from the entire hospital.

Investigators recorded when patients received advanced diagnostic tests that were ordered by specialists. These tests, however, were not included in analyses of how services received by SSU patients affected SSU success, because SSU attending physicians were only indirectly involved in whether or not patients received these tests. Treatments were considered acute care treatments if they were commonly administered only in acute care settings, such as heparin for unstable angina or intravenous furosemide for pulmonary edema.

SSU Success

The SSU was designed to care for patients during brief stays and without eventual admission to traditional inpatient services. Therefore, we used patients' LOS and whether or not patients were admitted to traditional inpatient services as measures of SSU success. LOS was calculated from the time patients arrived in the SSU until the time they left. Therefore, neither time spent in the ED before admission to the SSU nor time spent on traditional inpatient services (if needed) contributed to our definition of LOS. Individual SSU patients were considered successfully cared for in the SSU if their LOS was less than 72 hours and they were discharged directly home from the SSU. We explored associations between these outcomes and provisional diagnoses, risk assessments, and services received.

Data Analysis

LOS data were right‐skewed; therefore, we used the Mann‐Whitney test for comparisons between 2 groups and the Kruskal‐Wallis test for comparisons among 3 or more groups. To test for trends of median LOS among ordered groupings, we used the method of Cuzick.11 We used Pearson's chi‐square test to compare proportions of patients grouped into categories and the chi‐square test for trends with equal scoring to test for trends among ordered groupings.

We performed multiple logistic regression to explore which variables were associated with SSU success. The following 5 demographic variables from Table 1 were insignificant in all single‐variable and multiple‐variable regression models that we tested and were, therefore, removed from further analyses to create more parsimonious models: gender, language, ethnicity, race, and whether or not patients had a primary care provider. Our multiple logistic regression models were fitted by maximum likelihood methods. In all of these models, odds ratios (ORs) were adjusted for patient characteristics that included age (in years), insulin‐requiring diabetes mellitus (yes or no), SSU attending physician, day of the week of SSU admission (weekday or weekend), and hospitalization during the preceding year. Confidence intervals (CIs) for predicted probabilities were computed using the delta method. All analyses were conducted with Stata Statistical Software, Release 9 (StataCorp, College Station, TX).

Admission Characteristics of Enrolled Short‐Stay Unit Patients
  • NOTE: n = 751. Values are n (%) unless otherwise indicated.

  • Emergency department physicians listed an additional provisional diagnosis for 186 patients (25% of 751).

  • Other cardiovascular diagnoses were syncope, arrhythmia, hypertension, positive stress test with high‐risk features, and possible cerebrovascular disease.

  • Noncardiovascular diagnoses were asthma, chronic obstructive pulmonary disease, cellulitis, pneumonia, anemia, allergic reaction, chronic vomiting syndrome, gross hematuria, headache, hypokalemia, psoas hematoma, and pyelonephritis.

Mean age, years (SD) (25th‐75th percentiles)58 (12) (49‐66)
Men389 (52)
Lacking a primary care provider256 (34)
Non‐English speaking217 (29)
Ethnicity is Hispanic or Latino105 (14)
Race is Black or African‐American480 (64)
Hospitalized within the preceding year322 (43)
Insulin‐requiring diabetes mellitus83 (11)
Previous coronary artery revascularization89 (12)
Provisional diagnosis* 
Possible acute coronary syndrome427 (57)
Heart failure214 (29)
Other cardiovascular62 (8)
Noncardiovascular48 (6)

Results

Subjects

During the 4‐month study period, 755 patients were admitted to the SSU. Among these patients, 4 were excluded from our study because they refused verbal consent. In the remaining study sample of 751 patients, all were included in the descriptions of patients' admission characteristics (Table 1), but 13 patients who left prematurely were excluded in both the descriptions of services received (Table 2) and the analyses of SSU success (Tables 3 and 4).

Services Received by Provisional Diagnosis
Service receivedPossible ACS n = 418 (%)Heart Failure n = 211 (%)Other Cardiovascular n = 61 (%)Noncardiovascular n = 48 (%)Total n = 738 (%)
  • NOTE: n = 738. Does not include 13 patients who prematurely left the SSU against medical advice. P values for Pearson's chi‐square test for differences in proportions across all four groups were all <0.001.

  • Abbreviations: ACS, acute coronary syndrome; ETT, exercise treadmill test.

  • Eighteen patients received both a resting echocardiogram and an ETT.

  • Two patients received both a stress imaging test and another limited access test. Other limited access tests included esophagogastroduodenoscopy, colonoscopy, brain or spine magnetic resonance imaging, abdominal ultrasonography, carotid artery ultrasonography, cardiac multiple gated acquisition scan, bone scintigraphy, cardiac pacemaker interrogation, pulmonary angiography, and ventilation‐perfusion scan; 6 patients received 2 such tests.

  • Stress imaging tests included myocardial perfusion imaging and stress echocardiography; 3 patients received 2 stress imaging studies.

  • Acute care treatments included intravenous furosemide, nebulized albuterol or ipratropium, treatment doses of heparin, intravenous antibiotics, and intravenous insulin; 10 patients received 2 or more acute care treatments.

  • Additional diagnostic tests that required arrangement by specialty consultants were not considered open or limited access tests. They included coronary angiography, transesophageal echocardiography, cardiac electrophysiology study, and electroencephalography.

Open access test*3759561343
Resting     
Echocardiography2959561339
ETT120507
Limited access test24810817
Stress imaging2157214
Acute care treatment227856039
Specialty consultation241220819
Any above service6893676975
Patient Outcome by Provisional Diagnosis and Services Received
Provisional Diagnosis and Services ReceivednMedian LOS [hours (IQR)]Stay Longer than 72 Hours (%)Admission to Traditional Inpatient Service (%)Stay Longer than 72 Hours or Admission to Traditional Inpatient Service (%)
  • NOTE: n = 738. Does not include 13 patients who left prematurely against medical advice. P values are for Pearson's chi‐square test unless otherwise indicated.

  • Abbreviations: ACS, acute coronary syndrome; CV, cardiovascular; IQR, interquartile range; LOS, length‐of‐stay.

  • Among the 9% of patients (66/738) who were admitted to the traditional inpatient services, SSU attending physicians' reasons for admission for 74% of patients (49/66) were to provide treatments (n = 32), diagnostic tests (n = 14), and ancillary services (n = 3) not provided in the SSU or because prolonged treatment courses were anticipated on the current therapies (n = 17). Prior to admission to traditional inpatient services, 21 patients (32% of 66) had received no advanced diagnostic tests, and 9 patients (14% of 66) had received no advanced diagnostic tests, acute care treatments, or specialty consultations.

  • P values are for differences among provisional diagnostic groupings.

  • Nonparametric Kruskal‐Wallis test.

  • Nonparametric Mann‐Whitney test.

  • Among the 184 patients who did not receive any of the above services, 172 (23% of 738) stayed less than 72 hours and did not require admission to traditional inpatient services. Provisional diagnoses for these 172 patients included very‐low risk possible ACS (n = 106), higher‐than‐very‐low risk possible ACS (n = 20), noncardiovascular diagnoses (n = 19), heart failure (n = 13), and other cardiovascular diagnoses (n = 14).

All patients73842 (22‐63)159*21
Possible ACS41837 (20‐57)131020
Heart failure21147 (34‐69)21927
Other CV6140 (21‐49)10511
Non‐CV4840 (22‐60)10213
P value <0.0010.040.180.01
Open access test     
Yes32046 (31‐67)17923
No41833 (20‐52)13920
P value <0.0010.150.870.43
Limited access test     
Yes12851 (42‐82)31633
No61038 (21‐55)121019
P value <0.001<0.0010.24<0.001
Acute care treatment     
Yes28746 (34‐69)211229
No45132 (20‐50)11716
P value <0.001<0.0010.01<0.001
Specialty consultation     
Yes14163 (40‐92)382852
No59738 (21‐50)10514
P value <0.001<0.001<0.001<0.001
Any above service     
Yes55446 (29‐68)191026
No18422 (16‐32)257
P value <0.001<0.0010.03<0.001
Factors Associated with Stays Longer than 72 Hours or Admissions to Traditional Inpatient Services: Multivariable Models
 Stay Longer than 72 HoursAdmission to Traditional Inpatient ServiceEither Outcome
OR*P valueOR*P valueOR*P value
  • NOTE: Models include all enrolled patients who did not leave prematurely against medical advice (n = 738).

  • Abbreviation: OR, odds ratio; SSU, short‐stay unit.

  • All ORs are adjusted for age, insulin‐requiring diabetes mellitus, whether or not patients were hospitalized during the last year, SSU attending physician, day of the week of SSU admission, and all other variables listed. For dichotomous variables, the OR represents a ratio of the odds for the group with the specified characteristic versus the odds for the group without that characteristic.

  • P values are Pearson's chi‐square test.

Heart failure2.30.011.10.771.90.02
Service received      
Open access test1.50.101.00.891.20.32
Limited access test5.1<0.0010.40.032.5<0.001
Acute care treatment1.70.071.40.311.60.05
Specialty consultation6.1<0.00113.1<0.0018.1<0.001

Admission Characteristics and Services Received

A narrow range of provisional diagnoses were listed by ED attending physicians and 641 patients (85% of 751) were grouped as having possible ACS or heart failure (Figure 2). Patients with these diagnoses were later risk‐stratified and, when pooled across risk strata, only 14 patients (2% of 641) exceeded the suggested admission‐location criterion for the SSU of lower than high risk. Despite the array and frequency of diagnostic and treatment services that patients received, SSU physicians worked mostly independently, requesting specialty consultations for only 19% of patients (141/738; Table 2).

SSU Success

The median LOS for all patients was 42 hours (interquartile range [IRQ] 22‐63) and 156 patients (21% of 738) had unsuccessful SSU stays (Table 3). The most common reason for an unsuccessful stay was a stay longer than 72 hours (71% of 156). Among the 66 patients who required admission to traditional inpatient services, nearly one‐half (48%) were admitted expressly to receive treatments not available in the SSU after having a specialty consult.

Patients' provisional diagnoses were associated with unsuccessful stays in bivariate analyses (Table 3). In addition, when patients were grouped into 3 risk stratums (very‐low, low, and intermediate‐and‐high), unsuccessful stays increased with increasing risk. For example, in patients with possible ACS, the proportion of unsuccessful stays increased from 17% of 306 very‐low risk patients to 27% of 55 intermediate‐and‐high risk patients (P value for trend = 0.012. Similarly, in patients with heart failure, the proportion of unsuccessful stays increased from 25% of 181 very‐low risk patients to 100% of 3 intermediate‐and‐high risk patients (P value for trend = 0.004).

However, in multiple variable models that simultaneously included patients' characteristics upon admission with services received during their SSU stay, only the provisional diagnosis of heart failure was associated with unsuccessful stays (OR, 1.9; 95% CI, 1.12‐3.18); risk assessments for possible ACS (P = 0.29) and heart failure (P = 0.32) were unimportant predictors of unsuccessful stays (Table 4). On the other hand, whether or not patients received diagnostic tests, acute care treatments, or specialty consultations were important predictors. In particular, patients who received specialty consultations were much more likely to require admission to traditional inpatient services than those patients who did not (OR, 13.1; 95% CI, 6.9‐24.9) and had a 52% chance of having an unsuccessful stay (95% CI, 42‐61%; Figure 3). In addition, the accessibility of a diagnostic test was inversely proportional to the chance of having a long stay; patients who received an open access test had a 12% chance of a long stay (95% CI, 8‐16%) whereas those who received a limited access test had a 29% chance of a long stay (95% CI, 20‐39%). Receiving acute care treatments was also a significant, though less important, predictor of an unsuccessful stay (Table 4).

Figure 3
Predicted probabilities of unsuccessful SSU stays from type of service received while in the SSU. The multivariable regression models used to generate these probabilities are described in Table 3. These models were constructed using all enrolled patients who did not leave prematurely against medical advice (n = 738) and were adjusted for age, insulin‐requiring diabetes mellitus, hospitalization during the last year, SSU attending physician, day of the week of SSU admission, provisional diagnosis of heart failure, and services received. The probability represents a patient's likelihood of a long stay, an eventual admission to traditional inpatient services, or either outcome having received a listed service. The vertical capped lines represent 95% confidence intervals for the probability estimates. Abbreviations: Consult., specialty consultation; Limited Ac., limited access test; Open Ac., open access test; Treat., acute care treatment.

Discussion

We found that the types of services received by patients during their SSU stays were stronger predictors of long stays and eventual admissions to traditional inpatient services than patients' characteristics upon admission to the SSU. This suggests that SSUs should be focused toward matching patients' anticipated needs with readily accessible services. For example, in our SSU, which cares for over 2,250 patients annually, more than 1,200 patients will receive diagnostic tests in a given year. Among these patients, those who receive a limited access test will be more than twice as likely to have long stays than those who receive an open access test (Figure 3). Though our conclusions may not be applicable to other settings, this study is the most comprehensive description of patients admitted to a hospitalist‐run SSU. In addition, our study is the first to demonstrate that diagnostic and consultative services are the most important predictors of successful stays in SSUs. This promotes the practical strategy that hospitalists who staff SSUs should focus administratively toward gaining access to these services.

Very few of our SSU patients did not fulfill the suggested requirements of our admission location guidelines. For example, only 2% of 691 patients with either possible ACS or heart failure were high risk (Figure 2). Despite this, 21% of our patients had stays longer than 72 hours or were admitted to traditional inpatient services. The paradoxically high proportion of unsuccessful stays among mostly very‐low and low risk patients simply reflects how the clinical risk models that we used were not designed to predict unsuccessful stays. Moreover, as our multiple variable models suggest, improvements in the selection process of candidate SSU patients are more likely to come from an ability to incorporate assessments of what services patients will receive rather than from assessments of their clinical risk (Table 4). Therefore, the immediate plans of the accepting SSU physicians, the physicians who will determine what services patients eventually receive, should be incorporated in the admission‐location decision process.

Three of our findings highlight how input from accepting SSU physiciansconveyed to ED physicians before their final admission‐location decisions are mademay improve the SSU patient selection process. First, 23% of our patients were discharged home after brief stays with no advanced tests, specialty consultations, or acute care treatments (Table 3). Though some of these patients may have required inpatient services other than the ones we recorded, most were admitted with very‐low risk possible ACS; if they required overnight stays at all, many of them may have been better cared for in the ED observation unit (Figure 1). Second, 74% of the patients who required admission to traditional inpatient services were admitted for services not readily available to patients in the SSU (Table 3). Among these patients, nearly one‐third (21/66) received no advanced diagnostic tests in the SSU. This suggests that these patients should have been admitted directly to the general medical wards; doing so may have improved efficiency and quality of care by reducing unnecessary handoffs between physicians. Both types of patientsthose with minimal inpatient needs and those with more needs than the SSU can providehighlight how incorporating accepting SSU physicians' plans may improve the SSU patient selection process. After all, those best equipped to determine if the SSU will meet (or exceed) the needs of candidate patients are the SSU physicians themselves.

Third, we found that whether or not SSU physicians required assistance from specialists was the strongest predictor of unsuccessful stays: when an accepting physician determined that a patient should receive a specialty consultation, that patient's chance of having an unsuccessful stay was over 50% (Figure 3). Our study was not designed to determine how specialty consultations were associated with unsuccessful stays. We did not, for example, record whether or not hospitalists changed their diagnostic, treatment, or admission plans because of specialists' recommendations.12 Therefore, we cannot conclude that specialty consultations actually caused long stays or traditional admissions. Nevertheless, when our SSU physicians did not manage patients independent of specialty consultations, we observed a high likelihood of unsuccessful stays. Because accepting SSU physicians are the ones who will determine whether or not they need assistance from specialists, weighing their immediate plans for specialty consultations into the admission‐location decision process may improve the efficiency of SSUs. Others have recognized the importance of specialty consultations in SSUs by directly incorporating specialists as coattending physicians.13

Our study had several limitations. First, we studied mostly patients with cardiovascular diagnoses. Predictors of success in SSUs that admit patients with different diagnostic profiles may be different. In particular, SSUs that admit patients with a wide array of diagnoses may find that matching patients' needs with readily accessible services is impractical, because these needs may be too wide‐ranging. Second, our study design was observational. However, other than seasonal variations in admission patterns, there was little room for selection bias because we enrolled all consecutive admissions over the 4‐month study period, which gives us more confidence in our results. Third, our study did not record whether or not ED physicians knowingly overrode the suggested admission‐location guidelines because of limited bed availability. Yet, if shortages of beds on traditional inpatient services were driving patients who were otherwise candidates for the general medical wards in to the SSU, then we would have expected higher‐risk patients and greater needs for limited access tests. Finally, our descriptions of patients' needs were based on what diagnostic, consultative, and treatment services patients actually received; yet these needs did not include diagnostic tests that were ordered but never performed. However, any missed needs would bias our results toward no association with unsuccessful stays, because unsuccessful stays would generally increase while patients await needed services.

Future research could address these limitations through an experimental trial of traditional admissions versus admission to a hospitalist‐run SSU. And, because hospitals are complex systems of health care delivery where changes in one patient care unit often affect others in unanticipated ways,14 the impact of SSUs on other patient care units that are closely connected to SSUs, such as EDs and the general medical wards (Figure 1), should be simultaneously observed. For example, though our findings suggest that the accessibility of diagnostic tests should parallel ordering SSU physicians' needs for those tests, making all diagnostic tests open to SSU physicians may result in shortsightedly lengthening the stays of patients in other care units. Future research should also observe the decision‐making process of both the physicians who make admission‐location decisions (ED physicians) and those who determine the eventual plans for patients in the SSU (hospitalists). Accepting physicians from other patient care units have found improved outcomes of efficiency when they were involved in the complex process of deciding where to admit patients.15, 16 After an initial evaluation of a candidate SSU patient in the ED, a hospitalist who staffs both the general medical wards and the SSU would be uniquely well‐positioned to help an ED physician decide where a patient's needs would be best met. Although ED physicians will rightly be concerned that consulting SSU hospitalists may slow patient flow, hands‐on consultations of candidate SSU patients, who have a narrow range of diagnoses and low‐risk profiles, would likely be brief. In addition, because many SSUs are conveniently adjacent to EDs, the burden of communication may be minor.1 To address these questions, hospitalists who staff SSUs must continue the observed trend of working collaboratively with ED physicians.15, 17, 18

Acknowledgements

The authors thank Arthur T. Evans and Brendan M. Reilly for their insightful review of the manuscript. The authors also thank Zhaotai Cui for his assistance with statistical programming.

References
  1. Taylor DMcD,Bennett DM,Cameron PA.A paradigm shift in the nature of care provision in emergency departments.Emerg Med J.2004;21:681684.
  2. Brady W,Harrigan R,Chan T.Acute coronary syndromes. In: Marx J, Hockberger R, Walls R, eds.Rosen's emergency medicine. Concepts and clinical practice.6th ed.Edinburgh:Mosby Elsevier;2006:11541199.
  3. Darves B.Taking charge of observation units for better patient flow.Todays Hospitalist Mag.2007;5(7):1620.
  4. Barbado Ajo MJ,Jimeno Carruez A,Ostolaza Vázquez JM, et al.Unidad de corta estancia dependiente de Medicina Interna.An Med Interna.1999;16:504510.
  5. Diz‐Lois Palomares MT,de la Inglesia Martinez F,Nicolás Miguel R,Pellicer Vázquez C Ramos Polledo V,Diz‐Lois Martinez F.Factors that predict unplanned hospital readmission of patients discharged from a short stay medical unit.An Med Interna.2002;19:221225.
  6. Abenhaim HA,Kahn SR,Raffoul J, et al.Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital.CMAJ.2000;163(11):14771480.
  7. Reilly BM,Evans AT,Schaider JJ,Wang Y.Triage of patients with chest pain in the emergency department: a comparative study of physicians' decisions.Am J Med.2002;112:95103.
  8. Cook RI,Render M,Woods DD.Gaps in the continuity of care and progress on patient safety.BMJ.2000;320:791794.
  9. Goldman L,Cook EF,Johnson PA, et al.Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain.N Engl J Med.1996;334:14981504.
  10. Fonarow GC,Adams KF,Abraham WT, et al.Risk stratification for in‐hospital mortality in acutely decompensated heart failure.JAMA.2005;293:572580.
  11. Cuzick J.A Wilcoxon‐type test for trend.Stat Med.1985;4:8790.
  12. Braham RL,Ron A,Ruchlin HS,Hollenberg JP,Pompei P,Charlson ME.Diagnostic test restraint and the specialty consultation.J Gen Intern Med.1990;5:95103.
  13. Krantz MJ,Zwant O,Rowan SB, et al.A cooperative care model: cardiologists and hospitalists reduce length of stay in a chest pain observation unit.Crit Pathw Cardiol.2005;4:5558.
  14. Black D,Pearson M.Average length of stay, delayed discharge, and hospital congestion.BMJ.2002;325:610611.
  15. Halpert A.An internist in the emergency department: the IM facilitator program.HMO Pract.1991;10:4243.
  16. Multz AS,Chalfin DB,Samson IM, et al.A “closed” medical intensive care unit (MICU) improves resource utilization when compared with an “open” MICU.Am J Respir Crit Care Med.1998;157:14681473.
  17. Darves B.Hospitalists' new role in the ED: “clog busters.”Todays Hospitalist Mag.2005;3(8):1518.
  18. Sattinger A.Kindred spirits: ED doctors, hospitalists forge a critical collaboration.Hospitalist.2007;11(7):1,16–20.
References
  1. Taylor DMcD,Bennett DM,Cameron PA.A paradigm shift in the nature of care provision in emergency departments.Emerg Med J.2004;21:681684.
  2. Brady W,Harrigan R,Chan T.Acute coronary syndromes. In: Marx J, Hockberger R, Walls R, eds.Rosen's emergency medicine. Concepts and clinical practice.6th ed.Edinburgh:Mosby Elsevier;2006:11541199.
  3. Darves B.Taking charge of observation units for better patient flow.Todays Hospitalist Mag.2007;5(7):1620.
  4. Barbado Ajo MJ,Jimeno Carruez A,Ostolaza Vázquez JM, et al.Unidad de corta estancia dependiente de Medicina Interna.An Med Interna.1999;16:504510.
  5. Diz‐Lois Palomares MT,de la Inglesia Martinez F,Nicolás Miguel R,Pellicer Vázquez C Ramos Polledo V,Diz‐Lois Martinez F.Factors that predict unplanned hospital readmission of patients discharged from a short stay medical unit.An Med Interna.2002;19:221225.
  6. Abenhaim HA,Kahn SR,Raffoul J, et al.Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital.CMAJ.2000;163(11):14771480.
  7. Reilly BM,Evans AT,Schaider JJ,Wang Y.Triage of patients with chest pain in the emergency department: a comparative study of physicians' decisions.Am J Med.2002;112:95103.
  8. Cook RI,Render M,Woods DD.Gaps in the continuity of care and progress on patient safety.BMJ.2000;320:791794.
  9. Goldman L,Cook EF,Johnson PA, et al.Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain.N Engl J Med.1996;334:14981504.
  10. Fonarow GC,Adams KF,Abraham WT, et al.Risk stratification for in‐hospital mortality in acutely decompensated heart failure.JAMA.2005;293:572580.
  11. Cuzick J.A Wilcoxon‐type test for trend.Stat Med.1985;4:8790.
  12. Braham RL,Ron A,Ruchlin HS,Hollenberg JP,Pompei P,Charlson ME.Diagnostic test restraint and the specialty consultation.J Gen Intern Med.1990;5:95103.
  13. Krantz MJ,Zwant O,Rowan SB, et al.A cooperative care model: cardiologists and hospitalists reduce length of stay in a chest pain observation unit.Crit Pathw Cardiol.2005;4:5558.
  14. Black D,Pearson M.Average length of stay, delayed discharge, and hospital congestion.BMJ.2002;325:610611.
  15. Halpert A.An internist in the emergency department: the IM facilitator program.HMO Pract.1991;10:4243.
  16. Multz AS,Chalfin DB,Samson IM, et al.A “closed” medical intensive care unit (MICU) improves resource utilization when compared with an “open” MICU.Am J Respir Crit Care Med.1998;157:14681473.
  17. Darves B.Hospitalists' new role in the ED: “clog busters.”Todays Hospitalist Mag.2005;3(8):1518.
  18. Sattinger A.Kindred spirits: ED doctors, hospitalists forge a critical collaboration.Hospitalist.2007;11(7):1,16–20.
Issue
Journal of Hospital Medicine - 4(5)
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Journal of Hospital Medicine - 4(5)
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276-284
Page Number
276-284
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A hospitalist‐run short‐stay unit: Features that predict length‐of‐stay and eventual admission to traditional inpatient services
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A hospitalist‐run short‐stay unit: Features that predict length‐of‐stay and eventual admission to traditional inpatient services
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consultation, health facility environment, hospital units, hospitalists
Legacy Keywords
consultation, health facility environment, hospital units, hospitalists
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