The Double-Edged Sword of Doctor Speak

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Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).

Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.

The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."

Not surprising, the perceived pain was found to be significantly greater after the latter statement.

German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.

The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.

Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."

The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.

There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.

There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.

Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction

Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.

The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.

Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.

Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.

The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.

When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."

There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.

Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."

Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.

Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.

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Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).

Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.

The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."

Not surprising, the perceived pain was found to be significantly greater after the latter statement.

German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.

The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.

Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."

The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.

There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.

There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.

Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction

Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.

The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.

Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.

Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.

The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.

When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."

There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.

Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."

Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.

Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.

Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).

Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.

The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."

Not surprising, the perceived pain was found to be significantly greater after the latter statement.

German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.

The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.

Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."

The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.

There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.

There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.

Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction

Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.

The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.

Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.

Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.

The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.

When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."

There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.

Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."

Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.

Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.

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Top Five Targets for Primary Care

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Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.

Why is that the case?

ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.

Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."

Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.

Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.

Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.

Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.

Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.

These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].

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Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.

Why is that the case?

ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.

Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."

Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.

Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.

Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.

Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.

Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.

These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].

Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.

Why is that the case?

ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.

Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."

Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.

Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.

Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.

Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.

Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.

These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].

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Southern California Hospitals Using BOOST Model Report Readmission Rate Reductions

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Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.

Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.

One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA

CE, medical director of the public L.A. Care Health Plan, which co-sponsored the readmissions collaborative. Dr. Wanski, a practicing endocrinologist and a hospitalist at California Hospital Medical Center in Los Angeles, says L.A. Care will be testing the use of hospitalists at some of its contracted acute-care facilities starting in July. (Click here to listen to more of Dr. Wanski’s interview.)

At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.

“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”

Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.

“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.

Larry Beresford is a freelance writer in Oakland, Calif.

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Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.

Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.

One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA

CE, medical director of the public L.A. Care Health Plan, which co-sponsored the readmissions collaborative. Dr. Wanski, a practicing endocrinologist and a hospitalist at California Hospital Medical Center in Los Angeles, says L.A. Care will be testing the use of hospitalists at some of its contracted acute-care facilities starting in July. (Click here to listen to more of Dr. Wanski’s interview.)

At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.

“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”

Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.

“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.

Larry Beresford is a freelance writer in Oakland, Calif.

Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.

Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.

One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA

CE, medical director of the public L.A. Care Health Plan, which co-sponsored the readmissions collaborative. Dr. Wanski, a practicing endocrinologist and a hospitalist at California Hospital Medical Center in Los Angeles, says L.A. Care will be testing the use of hospitalists at some of its contracted acute-care facilities starting in July. (Click here to listen to more of Dr. Wanski’s interview.)

At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.

“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”

Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.

“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.

Larry Beresford is a freelance writer in Oakland, Calif.

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Antegrade Beats Retrograde Enteroscopy in Small Bowel Disease

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Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.

Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.

In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.

A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).

The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.

Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.

Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).

The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).

With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).

The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).

Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).

The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."

Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.

"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.

One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.

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Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.

Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.

In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.

A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).

The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.

Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.

Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).

The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).

With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).

The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).

Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).

The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."

Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.

"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.

One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.

Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.

Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.

In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.

A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).

The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.

Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.

Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).

The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).

With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).

The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).

Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).

The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."

Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.

"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.

One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.

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Secondary prophylaxis reduces bleeding in hemophilia

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Secondary prophylaxis reduces bleeding in hemophilia

Bleeding finger

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.

The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.

These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).

“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.

“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”

Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.

After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event. 

There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds. 

In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.

The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.

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Bleeding finger

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.

The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.

These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).

“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.

“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”

Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.

After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event. 

There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds. 

In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.

The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.

Bleeding finger

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.

The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.

These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).

“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.

“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”

Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.

After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event. 

There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds. 

In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.

The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.

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Families Help Addicts Enter Treatment

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Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"

She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.

©Dmytro Panchenko/iStockphoto.com

"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"

Family-Focused Interventions

Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.

Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.

Community Reinforcement Approach Family Training (CRAFT)

CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.

Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).

A Relational Intervention Sequence for Engagement (ARISE)

ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.

The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.

Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).

Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:

 

 

• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.

• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.

• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.

• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.

• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.

• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.

• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.

• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.

Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.

Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.

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Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"

She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.

©Dmytro Panchenko/iStockphoto.com

"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"

Family-Focused Interventions

Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.

Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.

Community Reinforcement Approach Family Training (CRAFT)

CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.

Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).

A Relational Intervention Sequence for Engagement (ARISE)

ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.

The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.

Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).

Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:

 

 

• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.

• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.

• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.

• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.

• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.

• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.

• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.

• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.

Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.

Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.

Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"

She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.

©Dmytro Panchenko/iStockphoto.com

"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"

Family-Focused Interventions

Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.

Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.

Community Reinforcement Approach Family Training (CRAFT)

CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.

Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).

A Relational Intervention Sequence for Engagement (ARISE)

ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.

The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.

Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).

Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:

 

 

• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.

• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.

• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.

• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.

• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.

• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.

• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.

• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.

Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.

Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.

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Localizing General Medical Teams

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Impact of localizing general medical teams to a single nursing unit

Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

Setting

We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.

Intervention

Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.

Transition

April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.

Patient Assignment

The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.

The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.

Evaluation

Clinical Outcomes

Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.

Inclusion Criteria

We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.

Exclusion Criteria

From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.

Data Collection

We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).

We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.

Data Analysis

We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.

We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.

Productivity and Workflow Measures

We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.

Data Collection

We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.

During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.

Data Analysis

For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.

Statistical Software

We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).

Ethical Concerns

The study protocol was approved by our institutional review board.

RESULTS

Study Population

There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.

Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).

Characteristics of Patients Admitted to Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: COPD, chronic obstructive pulmonary disease; ED, emergency department; ICU, intensive care unit; IQR, interquartile range; n, number; n/a, not applicable; UTI, urinary tract infection; w/cm, with complications.

Patients783565478 
Age median (IQR)57 (4575)57 (4573)56 (4470)0.186
Age groups, n (%)    
<3065 (8.3)37 (6.6)46 (9.6) 
303976 (9.7)62 (11.0)47 (9.8) 
4049114 (14.6)85 (15.0)68 (14.2) 
5059162 (20.7)124 (22.0)118 (24.7)0.145
6069119 (15.2)84 (14.9)76 (16.0) 
7079100 (12.8)62 (11.0)58 (12.1) 
8089113 (14.4)95 (16.8)51 (10.7) 
>8934 (4.3)16 (2.88)14 (2.9) 
Female gender, n (%)434 (55.4)327 (57.9)264 (55.2)0.602
Race: Black, n (%)285 (36.4)229 (40.5)200 (41.8)0.111
Observation status, n (%)165 (21.1)108 (19.1)108 (22.6)0.380
Insurance, n (%)    
Commercial171 (21.8)101 (17.9)101 (21.1) 
Medicare376 (48.0)278 (49.2)218 (45.6)0.225
Medicaid179 (22.8)126 (22.3)117 (24.5) 
Uninsured54 (7.3)60 (10.6)42 (8.8) 
Weekend admission, n (%)137 (17.5)116 (20.5)65 (13.6)0.013
Weekend discharge, n (%)132 (16.9)107 (18.9)91 (19.0)0.505
Source of admission    
ED, n (%)654 (83.5)450 (79.7)370 (77.4)0.022
No ICU stay, n (%)600 (76.6)440 (77.9)383 (80.1)0.348
Admission time, n (%)    
00000559239 (30.5)208 (36.8)172 (36.0) 
06001159296 (37.8)157 (27.8)154 (32.2)0.007
12001759183 (23.4)147 (26.0)105 (22.0) 
1800235965 (8.3)53 (9.4)47 (9.8) 
Discharge time, n (%)    
0000115967 (8.6)45 (8.0)43 (9.0) 
12001759590 (75.4)417 (73.8)364 (76.2)0.658
18002359126 (16.1)103 (18.2)71 (14.9) 
Inpatient deaths, n13136 
Top 5 primary diagnoses (%)    
1Chest pain (11.5)Chest pain (13.3)Chest pain (11.9) 
2Septicemia (6.4)Septicemia (5.1)Septicemia (3.8) 
3Diabetes w/cm (4.6)Pneumonia (4.9)Diabetes w/cm (3.3)n/a
4Pneumonia (2.8)Diabetes w/cm (4.1)Pneumonia (3.3) 
5UTI (2.7)COPD (3.2)UTI (2.9) 

Clinical Outcomes

Unadjusted Analyses

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).

Unadjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: ICU, intensive care unit; IQR, interquartile range; n, number; $, United States dollars.

30‐day readmissions n (%)118 (15.3)69 (12.5)66 (14.0)0.346
Charges: excluding patients initially admitted to ICU    
Median (IQR) in $9346 (621614,520)9724 (665715,390)9902 (661115,670)0.393
Charges: excluding all patients with an ICU stay    
Median (IQR) in $9270 (618713,990)9509 (660114,940)9846 (658015,400)0.283
Length of stay: excluding patients initially admitted to ICU    
Median (IQR) in days1.81 (1.223.35)2.16 (1.214.02)1.89 (1.193.50)0.214
Length of stay: excluding all patients with an ICU stay    
Median (IQR) in days1.75 (1.203.26)2.12 (1.203.74)1.84 (1.193.42)0.236

Adjusted Analysis

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).

Adjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; OR, odds ratio.

30‐day risk of readmission OR (CI)0.85 (0.611.19)0.94 (0.651.37)0.630
P value0.3510.751 
Charges: excluding patients initially admitted to ICU   
% change2% higher4% lower0.367
(CI)(6% lower to 11% higher)(12% lower to 5%higher) 
P value0.5720.427 
Charges: excluding all patients with an ICU stay   
% change2% higher5% lower0.314
(CI)(6% lower to 10% higher)(13% lower to 4% higher) 
P value0.6950.261 
Length of stay: excluding patients initially admitted to ICU   
% change11% higher9% higher0.105
(CI)(1% to 22% higher)(3% lower to 21% higher) 
P value0.0380.138 
Length of stay: excluding all patients with an ICU stay   
% change10% higher8% higher0.133
(CI)(0% to 22% higher)(3% lower to 20% higher) 
P value0.0470.171 

Productivity and Workflow Measures

Unadjusted Analyses

The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).

Unadjusted Comparisons of Productivity and Workflow Measures Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: IQR, interquartile range; RVU, relative value unit; SD, standard deviation.

Pages received/day (7 AM6 PM) Median (IQR)No data15 (921)28 (12.540)<0.001
Total encounters/day Median (IQR)10 (813)12 (1013)11 (913)<0.001
RVU/day    
Mean (SD)19.9 (6.76)22.6 (5.6)21.2 (6.7)<0.001
Steps/day Median (IQR)No data4661 (3922 5166)5554 (50606544)<0.001

Adjusted Analysis

On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).

Adjusted Comparisons of Productivity and Workflow Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI 95%, confidence interval; N, number; RVU, relative value units.

Pages received (7 AM 6 PM) %(CI)No data51% fewer (4854) 
P value P < 0.001 
Total encounters0.89 more1.02 more 
N (CI)(0.371.41)(0.461.58) 
P valueP < 0.001P < 0.001P < 0.001
RVU/day2.20 more1.36 more 
N (CI)(1.103.29)(0.172.55) 
P valueP < 0.001P = 0.024P < 0.001
Steps/day 1186 fewer (791 more to 
N (CI)No data3164 fewer) 
P value P = 0.240 

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

  • Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

  • Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

  • Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

 

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

Acknowledgements

Disclosure: Nothing to report.

Files
References
  1. Bush RW. Reducing waste in US health care systems. JAMA. 2007;297(8):871874.
  2. O'Leary KJ, Liebovitz DM, Baker DW. How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):8893.
  3. O'Leary K, Wayne D, Landler M, et al. Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  4. O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678684.
  5. Roy CL, Liang CL, Lund M, et al. Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361368.
  6. Findlay JM, Keogh MJ, Boulton C, Forward DP, Moran CG. Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393398.
  7. Kocher R, Sahni NR. Rethinking health care labor. N Engl J Med. 2011;365(15):13701372.
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Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

Setting

We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.

Intervention

Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.

Transition

April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.

Patient Assignment

The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.

The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.

Evaluation

Clinical Outcomes

Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.

Inclusion Criteria

We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.

Exclusion Criteria

From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.

Data Collection

We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).

We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.

Data Analysis

We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.

We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.

Productivity and Workflow Measures

We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.

Data Collection

We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.

During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.

Data Analysis

For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.

Statistical Software

We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).

Ethical Concerns

The study protocol was approved by our institutional review board.

RESULTS

Study Population

There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.

Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).

Characteristics of Patients Admitted to Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: COPD, chronic obstructive pulmonary disease; ED, emergency department; ICU, intensive care unit; IQR, interquartile range; n, number; n/a, not applicable; UTI, urinary tract infection; w/cm, with complications.

Patients783565478 
Age median (IQR)57 (4575)57 (4573)56 (4470)0.186
Age groups, n (%)    
<3065 (8.3)37 (6.6)46 (9.6) 
303976 (9.7)62 (11.0)47 (9.8) 
4049114 (14.6)85 (15.0)68 (14.2) 
5059162 (20.7)124 (22.0)118 (24.7)0.145
6069119 (15.2)84 (14.9)76 (16.0) 
7079100 (12.8)62 (11.0)58 (12.1) 
8089113 (14.4)95 (16.8)51 (10.7) 
>8934 (4.3)16 (2.88)14 (2.9) 
Female gender, n (%)434 (55.4)327 (57.9)264 (55.2)0.602
Race: Black, n (%)285 (36.4)229 (40.5)200 (41.8)0.111
Observation status, n (%)165 (21.1)108 (19.1)108 (22.6)0.380
Insurance, n (%)    
Commercial171 (21.8)101 (17.9)101 (21.1) 
Medicare376 (48.0)278 (49.2)218 (45.6)0.225
Medicaid179 (22.8)126 (22.3)117 (24.5) 
Uninsured54 (7.3)60 (10.6)42 (8.8) 
Weekend admission, n (%)137 (17.5)116 (20.5)65 (13.6)0.013
Weekend discharge, n (%)132 (16.9)107 (18.9)91 (19.0)0.505
Source of admission    
ED, n (%)654 (83.5)450 (79.7)370 (77.4)0.022
No ICU stay, n (%)600 (76.6)440 (77.9)383 (80.1)0.348
Admission time, n (%)    
00000559239 (30.5)208 (36.8)172 (36.0) 
06001159296 (37.8)157 (27.8)154 (32.2)0.007
12001759183 (23.4)147 (26.0)105 (22.0) 
1800235965 (8.3)53 (9.4)47 (9.8) 
Discharge time, n (%)    
0000115967 (8.6)45 (8.0)43 (9.0) 
12001759590 (75.4)417 (73.8)364 (76.2)0.658
18002359126 (16.1)103 (18.2)71 (14.9) 
Inpatient deaths, n13136 
Top 5 primary diagnoses (%)    
1Chest pain (11.5)Chest pain (13.3)Chest pain (11.9) 
2Septicemia (6.4)Septicemia (5.1)Septicemia (3.8) 
3Diabetes w/cm (4.6)Pneumonia (4.9)Diabetes w/cm (3.3)n/a
4Pneumonia (2.8)Diabetes w/cm (4.1)Pneumonia (3.3) 
5UTI (2.7)COPD (3.2)UTI (2.9) 

Clinical Outcomes

Unadjusted Analyses

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).

Unadjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: ICU, intensive care unit; IQR, interquartile range; n, number; $, United States dollars.

30‐day readmissions n (%)118 (15.3)69 (12.5)66 (14.0)0.346
Charges: excluding patients initially admitted to ICU    
Median (IQR) in $9346 (621614,520)9724 (665715,390)9902 (661115,670)0.393
Charges: excluding all patients with an ICU stay    
Median (IQR) in $9270 (618713,990)9509 (660114,940)9846 (658015,400)0.283
Length of stay: excluding patients initially admitted to ICU    
Median (IQR) in days1.81 (1.223.35)2.16 (1.214.02)1.89 (1.193.50)0.214
Length of stay: excluding all patients with an ICU stay    
Median (IQR) in days1.75 (1.203.26)2.12 (1.203.74)1.84 (1.193.42)0.236

Adjusted Analysis

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).

Adjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; OR, odds ratio.

30‐day risk of readmission OR (CI)0.85 (0.611.19)0.94 (0.651.37)0.630
P value0.3510.751 
Charges: excluding patients initially admitted to ICU   
% change2% higher4% lower0.367
(CI)(6% lower to 11% higher)(12% lower to 5%higher) 
P value0.5720.427 
Charges: excluding all patients with an ICU stay   
% change2% higher5% lower0.314
(CI)(6% lower to 10% higher)(13% lower to 4% higher) 
P value0.6950.261 
Length of stay: excluding patients initially admitted to ICU   
% change11% higher9% higher0.105
(CI)(1% to 22% higher)(3% lower to 21% higher) 
P value0.0380.138 
Length of stay: excluding all patients with an ICU stay   
% change10% higher8% higher0.133
(CI)(0% to 22% higher)(3% lower to 20% higher) 
P value0.0470.171 

Productivity and Workflow Measures

Unadjusted Analyses

The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).

Unadjusted Comparisons of Productivity and Workflow Measures Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: IQR, interquartile range; RVU, relative value unit; SD, standard deviation.

Pages received/day (7 AM6 PM) Median (IQR)No data15 (921)28 (12.540)<0.001
Total encounters/day Median (IQR)10 (813)12 (1013)11 (913)<0.001
RVU/day    
Mean (SD)19.9 (6.76)22.6 (5.6)21.2 (6.7)<0.001
Steps/day Median (IQR)No data4661 (3922 5166)5554 (50606544)<0.001

Adjusted Analysis

On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).

Adjusted Comparisons of Productivity and Workflow Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI 95%, confidence interval; N, number; RVU, relative value units.

Pages received (7 AM 6 PM) %(CI)No data51% fewer (4854) 
P value P < 0.001 
Total encounters0.89 more1.02 more 
N (CI)(0.371.41)(0.461.58) 
P valueP < 0.001P < 0.001P < 0.001
RVU/day2.20 more1.36 more 
N (CI)(1.103.29)(0.172.55) 
P valueP < 0.001P = 0.024P < 0.001
Steps/day 1186 fewer (791 more to 
N (CI)No data3164 fewer) 
P value P = 0.240 

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

  • Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

  • Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

  • Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

 

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

Acknowledgements

Disclosure: Nothing to report.

Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.

O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.

Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.

In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.

METHODS

Setting

We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.

Intervention

Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.

Transition

April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.

Patient Assignment

The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.

The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.

Evaluation

Clinical Outcomes

Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.

Inclusion Criteria

We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.

Exclusion Criteria

From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.

Data Collection

We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).

We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.

Data Analysis

We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.

We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.

Productivity and Workflow Measures

We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.

Data Collection

We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.

During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.

Data Analysis

For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.

Statistical Software

We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).

Ethical Concerns

The study protocol was approved by our institutional review board.

RESULTS

Study Population

There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.

Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).

Characteristics of Patients Admitted to Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: COPD, chronic obstructive pulmonary disease; ED, emergency department; ICU, intensive care unit; IQR, interquartile range; n, number; n/a, not applicable; UTI, urinary tract infection; w/cm, with complications.

Patients783565478 
Age median (IQR)57 (4575)57 (4573)56 (4470)0.186
Age groups, n (%)    
<3065 (8.3)37 (6.6)46 (9.6) 
303976 (9.7)62 (11.0)47 (9.8) 
4049114 (14.6)85 (15.0)68 (14.2) 
5059162 (20.7)124 (22.0)118 (24.7)0.145
6069119 (15.2)84 (14.9)76 (16.0) 
7079100 (12.8)62 (11.0)58 (12.1) 
8089113 (14.4)95 (16.8)51 (10.7) 
>8934 (4.3)16 (2.88)14 (2.9) 
Female gender, n (%)434 (55.4)327 (57.9)264 (55.2)0.602
Race: Black, n (%)285 (36.4)229 (40.5)200 (41.8)0.111
Observation status, n (%)165 (21.1)108 (19.1)108 (22.6)0.380
Insurance, n (%)    
Commercial171 (21.8)101 (17.9)101 (21.1) 
Medicare376 (48.0)278 (49.2)218 (45.6)0.225
Medicaid179 (22.8)126 (22.3)117 (24.5) 
Uninsured54 (7.3)60 (10.6)42 (8.8) 
Weekend admission, n (%)137 (17.5)116 (20.5)65 (13.6)0.013
Weekend discharge, n (%)132 (16.9)107 (18.9)91 (19.0)0.505
Source of admission    
ED, n (%)654 (83.5)450 (79.7)370 (77.4)0.022
No ICU stay, n (%)600 (76.6)440 (77.9)383 (80.1)0.348
Admission time, n (%)    
00000559239 (30.5)208 (36.8)172 (36.0) 
06001159296 (37.8)157 (27.8)154 (32.2)0.007
12001759183 (23.4)147 (26.0)105 (22.0) 
1800235965 (8.3)53 (9.4)47 (9.8) 
Discharge time, n (%)    
0000115967 (8.6)45 (8.0)43 (9.0) 
12001759590 (75.4)417 (73.8)364 (76.2)0.658
18002359126 (16.1)103 (18.2)71 (14.9) 
Inpatient deaths, n13136 
Top 5 primary diagnoses (%)    
1Chest pain (11.5)Chest pain (13.3)Chest pain (11.9) 
2Septicemia (6.4)Septicemia (5.1)Septicemia (3.8) 
3Diabetes w/cm (4.6)Pneumonia (4.9)Diabetes w/cm (3.3)n/a
4Pneumonia (2.8)Diabetes w/cm (4.1)Pneumonia (3.3) 
5UTI (2.7)COPD (3.2)UTI (2.9) 

Clinical Outcomes

Unadjusted Analyses

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).

Unadjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: ICU, intensive care unit; IQR, interquartile range; n, number; $, United States dollars.

30‐day readmissions n (%)118 (15.3)69 (12.5)66 (14.0)0.346
Charges: excluding patients initially admitted to ICU    
Median (IQR) in $9346 (621614,520)9724 (665715,390)9902 (661115,670)0.393
Charges: excluding all patients with an ICU stay    
Median (IQR) in $9270 (618713,990)9509 (660114,940)9846 (658015,400)0.283
Length of stay: excluding patients initially admitted to ICU    
Median (IQR) in days1.81 (1.223.35)2.16 (1.214.02)1.89 (1.193.50)0.214
Length of stay: excluding all patients with an ICU stay    
Median (IQR) in days1.75 (1.203.26)2.12 (1.203.74)1.84 (1.193.42)0.236

Adjusted Analysis

The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).

Adjusted Comparisons of Clinical Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI, confidence interval; ICU, intensive care unit; OR, odds ratio.

30‐day risk of readmission OR (CI)0.85 (0.611.19)0.94 (0.651.37)0.630
P value0.3510.751 
Charges: excluding patients initially admitted to ICU   
% change2% higher4% lower0.367
(CI)(6% lower to 11% higher)(12% lower to 5%higher) 
P value0.5720.427 
Charges: excluding all patients with an ICU stay   
% change2% higher5% lower0.314
(CI)(6% lower to 10% higher)(13% lower to 4% higher) 
P value0.6950.261 
Length of stay: excluding patients initially admitted to ICU   
% change11% higher9% higher0.105
(CI)(1% to 22% higher)(3% lower to 21% higher) 
P value0.0380.138 
Length of stay: excluding all patients with an ICU stay   
% change10% higher8% higher0.133
(CI)(0% to 22% higher)(3% lower to 20% higher) 
P value0.0470.171 

Productivity and Workflow Measures

Unadjusted Analyses

The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).

Unadjusted Comparisons of Productivity and Workflow Measures Between Localized Teams and Control Groups
 Historical ControlIntervention Localized TeamsConcurrent ControlP Value
  • Abbreviations: IQR, interquartile range; RVU, relative value unit; SD, standard deviation.

Pages received/day (7 AM6 PM) Median (IQR)No data15 (921)28 (12.540)<0.001
Total encounters/day Median (IQR)10 (813)12 (1013)11 (913)<0.001
RVU/day    
Mean (SD)19.9 (6.76)22.6 (5.6)21.2 (6.7)<0.001
Steps/day Median (IQR)No data4661 (3922 5166)5554 (50606544)<0.001

Adjusted Analysis

On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).

Adjusted Comparisons of Productivity and Workflow Outcomes Between Localized Teams and Control Groups
 Localized Teams in Comparison to 
 Historical ControlConcurrent ControlOverall P Value
  • Abbreviations: CI 95%, confidence interval; N, number; RVU, relative value units.

Pages received (7 AM 6 PM) %(CI)No data51% fewer (4854) 
P value P < 0.001 
Total encounters0.89 more1.02 more 
N (CI)(0.371.41)(0.461.58) 
P valueP < 0.001P < 0.001P < 0.001
RVU/day2.20 more1.36 more 
N (CI)(1.103.29)(0.172.55) 
P valueP < 0.001P = 0.024P < 0.001
Steps/day 1186 fewer (791 more to 
N (CI)No data3164 fewer) 
P value P = 0.240 

DISCUSSION

We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.

The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.

The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:

  • Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).

  • Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.

  • Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.

 

Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.

Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.

Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.

We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.

Acknowledgements

Disclosure: Nothing to report.

References
  1. Bush RW. Reducing waste in US health care systems. JAMA. 2007;297(8):871874.
  2. O'Leary KJ, Liebovitz DM, Baker DW. How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):8893.
  3. O'Leary K, Wayne D, Landler M, et al. Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  4. O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678684.
  5. Roy CL, Liang CL, Lund M, et al. Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361368.
  6. Findlay JM, Keogh MJ, Boulton C, Forward DP, Moran CG. Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393398.
  7. Kocher R, Sahni NR. Rethinking health care labor. N Engl J Med. 2011;365(15):13701372.
References
  1. Bush RW. Reducing waste in US health care systems. JAMA. 2007;297(8):871874.
  2. O'Leary KJ, Liebovitz DM, Baker DW. How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):8893.
  3. O'Leary K, Wayne D, Landler M, et al. Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):12231227.
  4. O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678684.
  5. Roy CL, Liang CL, Lund M, et al. Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361368.
  6. Findlay JM, Keogh MJ, Boulton C, Forward DP, Moran CG. Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393398.
  7. Kocher R, Sahni NR. Rethinking health care labor. N Engl J Med. 2011;365(15):13701372.
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Alcohol Withdrawal Admissions

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Multiple admissions for alcohol withdrawal

Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6

As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18

These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.

METHODS

The Mayo Clinic Institutional Review Board deemed the study exempt.

Patient Selection

The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Figure 1
Study design. Abbreviations: AWS, alcohol withdrawal syndrome; CIWA‐Ar, Clinical Institute Withdrawal Assessment—Alcohol Revised.

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.

Clinical Variables

Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.

Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.

Statistical Analysis

Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).

RESULTS

The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.

Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).

Univariate Analysis of Demographic Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: GED, General Educational Development; SD, standard deviation. *P 0.05 and significant.

Age, years (SD)47.85 (12.84)45.94 (12)0.170
Male, No. (%)122 (68)109 (77)0.080
Race/Ethnicity, No. (%)  0.270
White168 (93)132 (93) 
African American6 (3)3 (2) 
Asian0 (0)1 (1) 
Middle Eastern3 (2)0 (0) 
Other3 (2)6 (4) 
Relationship status, No. (%)  0.160
Divorced49 (27)55 (39)0.028*
Married54 (30)34 (24)0.230
Separated9 (5)4 (3)0.323
Single59 (33)38 (27)0.243
Widowed5 (3)3 (2)0.703
Committed4 (2)7 (5)0.188
Unknown0 (0)1 (1)0.259
Education, No. (%)  0.002*
High school graduate, GED, or less49 (28)67 (47) 
Some college or above89 (49)60 (42) 
Unknown41 (23)15 (11) 
Employment, No. (%)  0.290
Retired26 (14)12 (8) 
Employed72 (40)51 (36) 
Unemployed51 (28)51 (36) 
Homemaker9 (5)4 (3) 
Work disabled20 (11)23 (16) 
Student1 (1)0 (0) 
Unknown1 (1)1 (1) 
Univariate Analysis of Clinical Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: ALT, alanine aminotransferase; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised; ICU, intensive care unit; LOS, length of stay (days); RRT, rapid response team; SD, standard deviation. *P 0.05 and significant. Score >5. Score >3.

LOS, mean (SD)3.71 (7.10)2.72 (3.40)0.130
Charlson Comorbidity Index, mean (SD)1.7 (2.23)2.51 (2.90)0.005*
Medical comorbidity, No. (%)   
Diabetes mellitus6 (3)16 (11)0.005*
Cardiovascular disease6 (3)15 (11)0.050*
Cerebrovascular disease0 (0)3 (2)0.009*
Hypertension53 (30)36 (25)0.400
Cancer17 (7)10 (9)0.440
Psychiatric comorbidity, No. (%)97 (54)94 (66)0.026*
Adjustment disorder0 (0)6 (4)0.005*
Depressive disorder85 (47)76 (54)0.260
Bipolar disorder6 (3)10 (7)0.130
Psychotic disorder4 (2)6 (4)0.030*
Anxiety disorder30 (17)25 (18)0.820
Drug abuse4 (2)4 (3)0.730
Eating disorder0 (0)3 (2)0.050*
CIWA‐Ar scores   
CIWA‐Ar score (max), mean (SD)15 (8)20 (9)<0.000*
Component, mean (SD)   
Agitation20 (11)36 (25)0.001*
Anxiety23 (13)38 (27)0.001*
Auditory disturbance4 (2)9 (6)0.110
Headache11 (6)26 (18)0.001*
Nausea/vomiting5 (3)17 (12)0.003*
Orientation52 (29)72 (51)0.001*
Paroxysm/sweats9 (5)17 (12)0.023*
Tactile disturbance25 (14)54 (38)0.001*
Tremor35 (19)47 (33)0.004*
Visual disturbance54 (30)77 (54)0.001*
ALT (U/L), mean (SD)76 (85)101 (71)0.050*
Ammonia (mcg N/dl), mean (SD)25 (14)29 (29)0.530
Positive urine drug screen, No. (%)25 (14)49 (35)<0.001*
Tetrahydrocannabinol14 (56)19 (39) 
Cocaine8 (32)8 (16) 
Benzodiazepine6 (24)11 (22) 
Opiate4 (16)13 (26) 
Amphetamine2 (8)2 (4) 
Barbiturate1 (4)0 (0) 
Adverse event, No. (%)   
RRT1 (1)1 (1)0.866
ICU transfer32 (18)20 (14)0.550
Intubation12 (7)4 (3)0.890
Delirium tremens7 (4)4 (3)0.600
In‐hospital mortality0 (0)0 (0) 

Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).

Multivariate Analysis of Variables Associated With Multiple Admissions
VariableAdjusted Odds Ratio (95% CI)P Value
  • Abbreviations: CI, confidence interval; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised. *P 0.05 and significant.

High school education or less2.074 (1.219, 3.529)0.0071*
CIWA‐Ar score (max)1.074 (1.042, 1.107)<0.0001*
Charlson Comorbidity Index1.232 (1.088, 1.396)0.0010*
Psychiatric comorbidity1.757 (1.055, 2.928)0.0303*
Positive urine drug screen3.180 (1.740, 5.812)0.0002*

DISCUSSION

We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.

Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.

In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.

Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29

Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18

Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.

In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.

Files
References
  1. Moss M, Burnham EL. Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207S212.
  2. O'Brien JMLu B, Ali NA, et al. Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345350.
  3. Foy A, March S, Drinkwater V. Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360364.
  4. Sarff M, Gold JA. Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494S501.
  5. Moore M, Gray M. Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):5859.
  6. Louro Puerta R, Anton Otero E, Zuniga V Lorenzo. Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307309.
  7. Saitz R, Ghali WA, Moskowitz MA. The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):14461452.
  8. Monte R, Rabunal R, Casariego E, Lopez‐Agreda H, Mateos A, Pertega S. Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151158.
  9. Khan A, Levy P, DeHorn S, Miller W, Compton S. Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788790.
  10. Campos J, Roca L, Gude F, Gonzalez‐Quintela A. Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):11801186.
  11. Raven MC, Carrier ER, Lee J, Billings JC, Marr M, Gourevitch MN. Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):2230.
  12. Moos RH, Brennan PL, Mertens JR. Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173183.
  13. Moos RH, Mertens JR, Brennan PL. Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561570.
  14. Mertens JR, Weisner CM, Ray GT. Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842847.
  15. Luchansky B, He L, Krupski A, Stark KD. Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255270.
  16. Brennan PL, Kagay CR, Geppert JJ, Moos RH. Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891895.
  17. Tomasson K, Vaglum P. The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129136.
  18. Carrier E, McNeely J, Lobach I, Tay S, Gourevitch MN, Raven MC. Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116122.
  19. Sullivan JT, Sykora K, Schneiderman J, Naranjo CA, Sellers EM. Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):13531357.
  20. Hecksel KA, Bostwick JM, Jaeger TM, Cha SS. Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274279.
  21. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  22. Ponzer S, Johansson S‐E, Bergman B. A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):8388.
  23. Walker RD, Howard MO, Anderson B, et al. Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932937.
  24. Regier DA, Farmer ME, Rae DS, et al. Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):25112518.
  25. Greenfield SF, Sugarman DE, Muenz LR, Patterson MD, He DY, Weiss RD. The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):12781285.
  26. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830842.
  27. Shih M, Simon PA. Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521528.
  28. Saitz R, Horton NJ, Larson MJ, Winter M, Samet JH. Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):7078.
  29. Mertens JR, Flisher AJ, Satre DD, Weisner CM. The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):4553.
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Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6

As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18

These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.

METHODS

The Mayo Clinic Institutional Review Board deemed the study exempt.

Patient Selection

The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Figure 1
Study design. Abbreviations: AWS, alcohol withdrawal syndrome; CIWA‐Ar, Clinical Institute Withdrawal Assessment—Alcohol Revised.

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.

Clinical Variables

Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.

Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.

Statistical Analysis

Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).

RESULTS

The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.

Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).

Univariate Analysis of Demographic Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: GED, General Educational Development; SD, standard deviation. *P 0.05 and significant.

Age, years (SD)47.85 (12.84)45.94 (12)0.170
Male, No. (%)122 (68)109 (77)0.080
Race/Ethnicity, No. (%)  0.270
White168 (93)132 (93) 
African American6 (3)3 (2) 
Asian0 (0)1 (1) 
Middle Eastern3 (2)0 (0) 
Other3 (2)6 (4) 
Relationship status, No. (%)  0.160
Divorced49 (27)55 (39)0.028*
Married54 (30)34 (24)0.230
Separated9 (5)4 (3)0.323
Single59 (33)38 (27)0.243
Widowed5 (3)3 (2)0.703
Committed4 (2)7 (5)0.188
Unknown0 (0)1 (1)0.259
Education, No. (%)  0.002*
High school graduate, GED, or less49 (28)67 (47) 
Some college or above89 (49)60 (42) 
Unknown41 (23)15 (11) 
Employment, No. (%)  0.290
Retired26 (14)12 (8) 
Employed72 (40)51 (36) 
Unemployed51 (28)51 (36) 
Homemaker9 (5)4 (3) 
Work disabled20 (11)23 (16) 
Student1 (1)0 (0) 
Unknown1 (1)1 (1) 
Univariate Analysis of Clinical Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: ALT, alanine aminotransferase; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised; ICU, intensive care unit; LOS, length of stay (days); RRT, rapid response team; SD, standard deviation. *P 0.05 and significant. Score >5. Score >3.

LOS, mean (SD)3.71 (7.10)2.72 (3.40)0.130
Charlson Comorbidity Index, mean (SD)1.7 (2.23)2.51 (2.90)0.005*
Medical comorbidity, No. (%)   
Diabetes mellitus6 (3)16 (11)0.005*
Cardiovascular disease6 (3)15 (11)0.050*
Cerebrovascular disease0 (0)3 (2)0.009*
Hypertension53 (30)36 (25)0.400
Cancer17 (7)10 (9)0.440
Psychiatric comorbidity, No. (%)97 (54)94 (66)0.026*
Adjustment disorder0 (0)6 (4)0.005*
Depressive disorder85 (47)76 (54)0.260
Bipolar disorder6 (3)10 (7)0.130
Psychotic disorder4 (2)6 (4)0.030*
Anxiety disorder30 (17)25 (18)0.820
Drug abuse4 (2)4 (3)0.730
Eating disorder0 (0)3 (2)0.050*
CIWA‐Ar scores   
CIWA‐Ar score (max), mean (SD)15 (8)20 (9)<0.000*
Component, mean (SD)   
Agitation20 (11)36 (25)0.001*
Anxiety23 (13)38 (27)0.001*
Auditory disturbance4 (2)9 (6)0.110
Headache11 (6)26 (18)0.001*
Nausea/vomiting5 (3)17 (12)0.003*
Orientation52 (29)72 (51)0.001*
Paroxysm/sweats9 (5)17 (12)0.023*
Tactile disturbance25 (14)54 (38)0.001*
Tremor35 (19)47 (33)0.004*
Visual disturbance54 (30)77 (54)0.001*
ALT (U/L), mean (SD)76 (85)101 (71)0.050*
Ammonia (mcg N/dl), mean (SD)25 (14)29 (29)0.530
Positive urine drug screen, No. (%)25 (14)49 (35)<0.001*
Tetrahydrocannabinol14 (56)19 (39) 
Cocaine8 (32)8 (16) 
Benzodiazepine6 (24)11 (22) 
Opiate4 (16)13 (26) 
Amphetamine2 (8)2 (4) 
Barbiturate1 (4)0 (0) 
Adverse event, No. (%)   
RRT1 (1)1 (1)0.866
ICU transfer32 (18)20 (14)0.550
Intubation12 (7)4 (3)0.890
Delirium tremens7 (4)4 (3)0.600
In‐hospital mortality0 (0)0 (0) 

Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).

Multivariate Analysis of Variables Associated With Multiple Admissions
VariableAdjusted Odds Ratio (95% CI)P Value
  • Abbreviations: CI, confidence interval; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised. *P 0.05 and significant.

High school education or less2.074 (1.219, 3.529)0.0071*
CIWA‐Ar score (max)1.074 (1.042, 1.107)<0.0001*
Charlson Comorbidity Index1.232 (1.088, 1.396)0.0010*
Psychiatric comorbidity1.757 (1.055, 2.928)0.0303*
Positive urine drug screen3.180 (1.740, 5.812)0.0002*

DISCUSSION

We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.

Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.

In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.

Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29

Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18

Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.

In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.

Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6

As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18

These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.

METHODS

The Mayo Clinic Institutional Review Board deemed the study exempt.

Patient Selection

The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Figure 1
Study design. Abbreviations: AWS, alcohol withdrawal syndrome; CIWA‐Ar, Clinical Institute Withdrawal Assessment—Alcohol Revised.

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.

Clinical Variables

Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.

Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.

Statistical Analysis

Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).

RESULTS

The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.

Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).

Univariate Analysis of Demographic Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: GED, General Educational Development; SD, standard deviation. *P 0.05 and significant.

Age, years (SD)47.85 (12.84)45.94 (12)0.170
Male, No. (%)122 (68)109 (77)0.080
Race/Ethnicity, No. (%)  0.270
White168 (93)132 (93) 
African American6 (3)3 (2) 
Asian0 (0)1 (1) 
Middle Eastern3 (2)0 (0) 
Other3 (2)6 (4) 
Relationship status, No. (%)  0.160
Divorced49 (27)55 (39)0.028*
Married54 (30)34 (24)0.230
Separated9 (5)4 (3)0.323
Single59 (33)38 (27)0.243
Widowed5 (3)3 (2)0.703
Committed4 (2)7 (5)0.188
Unknown0 (0)1 (1)0.259
Education, No. (%)  0.002*
High school graduate, GED, or less49 (28)67 (47) 
Some college or above89 (49)60 (42) 
Unknown41 (23)15 (11) 
Employment, No. (%)  0.290
Retired26 (14)12 (8) 
Employed72 (40)51 (36) 
Unemployed51 (28)51 (36) 
Homemaker9 (5)4 (3) 
Work disabled20 (11)23 (16) 
Student1 (1)0 (0) 
Unknown1 (1)1 (1) 
Univariate Analysis of Clinical Variables and Multiple Admissions
VariableSingle Admission N = 180Multiple Admissions N = 142P Value
  • Abbreviations: ALT, alanine aminotransferase; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised; ICU, intensive care unit; LOS, length of stay (days); RRT, rapid response team; SD, standard deviation. *P 0.05 and significant. Score >5. Score >3.

LOS, mean (SD)3.71 (7.10)2.72 (3.40)0.130
Charlson Comorbidity Index, mean (SD)1.7 (2.23)2.51 (2.90)0.005*
Medical comorbidity, No. (%)   
Diabetes mellitus6 (3)16 (11)0.005*
Cardiovascular disease6 (3)15 (11)0.050*
Cerebrovascular disease0 (0)3 (2)0.009*
Hypertension53 (30)36 (25)0.400
Cancer17 (7)10 (9)0.440
Psychiatric comorbidity, No. (%)97 (54)94 (66)0.026*
Adjustment disorder0 (0)6 (4)0.005*
Depressive disorder85 (47)76 (54)0.260
Bipolar disorder6 (3)10 (7)0.130
Psychotic disorder4 (2)6 (4)0.030*
Anxiety disorder30 (17)25 (18)0.820
Drug abuse4 (2)4 (3)0.730
Eating disorder0 (0)3 (2)0.050*
CIWA‐Ar scores   
CIWA‐Ar score (max), mean (SD)15 (8)20 (9)<0.000*
Component, mean (SD)   
Agitation20 (11)36 (25)0.001*
Anxiety23 (13)38 (27)0.001*
Auditory disturbance4 (2)9 (6)0.110
Headache11 (6)26 (18)0.001*
Nausea/vomiting5 (3)17 (12)0.003*
Orientation52 (29)72 (51)0.001*
Paroxysm/sweats9 (5)17 (12)0.023*
Tactile disturbance25 (14)54 (38)0.001*
Tremor35 (19)47 (33)0.004*
Visual disturbance54 (30)77 (54)0.001*
ALT (U/L), mean (SD)76 (85)101 (71)0.050*
Ammonia (mcg N/dl), mean (SD)25 (14)29 (29)0.530
Positive urine drug screen, No. (%)25 (14)49 (35)<0.001*
Tetrahydrocannabinol14 (56)19 (39) 
Cocaine8 (32)8 (16) 
Benzodiazepine6 (24)11 (22) 
Opiate4 (16)13 (26) 
Amphetamine2 (8)2 (4) 
Barbiturate1 (4)0 (0) 
Adverse event, No. (%)   
RRT1 (1)1 (1)0.866
ICU transfer32 (18)20 (14)0.550
Intubation12 (7)4 (3)0.890
Delirium tremens7 (4)4 (3)0.600
In‐hospital mortality0 (0)0 (0) 

Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).

Multivariate Analysis of Variables Associated With Multiple Admissions
VariableAdjusted Odds Ratio (95% CI)P Value
  • Abbreviations: CI, confidence interval; CIWA‐Ar, Clinical Institute Withdrawal AssessmentAlcohol Revised. *P 0.05 and significant.

High school education or less2.074 (1.219, 3.529)0.0071*
CIWA‐Ar score (max)1.074 (1.042, 1.107)<0.0001*
Charlson Comorbidity Index1.232 (1.088, 1.396)0.0010*
Psychiatric comorbidity1.757 (1.055, 2.928)0.0303*
Positive urine drug screen3.180 (1.740, 5.812)0.0002*

DISCUSSION

We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.

Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.

In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.

Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29

Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18

Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.

In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.

References
  1. Moss M, Burnham EL. Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207S212.
  2. O'Brien JMLu B, Ali NA, et al. Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345350.
  3. Foy A, March S, Drinkwater V. Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360364.
  4. Sarff M, Gold JA. Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494S501.
  5. Moore M, Gray M. Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):5859.
  6. Louro Puerta R, Anton Otero E, Zuniga V Lorenzo. Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307309.
  7. Saitz R, Ghali WA, Moskowitz MA. The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):14461452.
  8. Monte R, Rabunal R, Casariego E, Lopez‐Agreda H, Mateos A, Pertega S. Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151158.
  9. Khan A, Levy P, DeHorn S, Miller W, Compton S. Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788790.
  10. Campos J, Roca L, Gude F, Gonzalez‐Quintela A. Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):11801186.
  11. Raven MC, Carrier ER, Lee J, Billings JC, Marr M, Gourevitch MN. Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):2230.
  12. Moos RH, Brennan PL, Mertens JR. Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173183.
  13. Moos RH, Mertens JR, Brennan PL. Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561570.
  14. Mertens JR, Weisner CM, Ray GT. Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842847.
  15. Luchansky B, He L, Krupski A, Stark KD. Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255270.
  16. Brennan PL, Kagay CR, Geppert JJ, Moos RH. Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891895.
  17. Tomasson K, Vaglum P. The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129136.
  18. Carrier E, McNeely J, Lobach I, Tay S, Gourevitch MN, Raven MC. Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116122.
  19. Sullivan JT, Sykora K, Schneiderman J, Naranjo CA, Sellers EM. Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):13531357.
  20. Hecksel KA, Bostwick JM, Jaeger TM, Cha SS. Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274279.
  21. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  22. Ponzer S, Johansson S‐E, Bergman B. A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):8388.
  23. Walker RD, Howard MO, Anderson B, et al. Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932937.
  24. Regier DA, Farmer ME, Rae DS, et al. Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):25112518.
  25. Greenfield SF, Sugarman DE, Muenz LR, Patterson MD, He DY, Weiss RD. The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):12781285.
  26. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830842.
  27. Shih M, Simon PA. Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521528.
  28. Saitz R, Horton NJ, Larson MJ, Winter M, Samet JH. Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):7078.
  29. Mertens JR, Flisher AJ, Satre DD, Weisner CM. The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):4553.
References
  1. Moss M, Burnham EL. Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207S212.
  2. O'Brien JMLu B, Ali NA, et al. Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345350.
  3. Foy A, March S, Drinkwater V. Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360364.
  4. Sarff M, Gold JA. Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494S501.
  5. Moore M, Gray M. Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):5859.
  6. Louro Puerta R, Anton Otero E, Zuniga V Lorenzo. Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307309.
  7. Saitz R, Ghali WA, Moskowitz MA. The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):14461452.
  8. Monte R, Rabunal R, Casariego E, Lopez‐Agreda H, Mateos A, Pertega S. Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151158.
  9. Khan A, Levy P, DeHorn S, Miller W, Compton S. Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788790.
  10. Campos J, Roca L, Gude F, Gonzalez‐Quintela A. Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):11801186.
  11. Raven MC, Carrier ER, Lee J, Billings JC, Marr M, Gourevitch MN. Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):2230.
  12. Moos RH, Brennan PL, Mertens JR. Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173183.
  13. Moos RH, Mertens JR, Brennan PL. Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561570.
  14. Mertens JR, Weisner CM, Ray GT. Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842847.
  15. Luchansky B, He L, Krupski A, Stark KD. Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255270.
  16. Brennan PL, Kagay CR, Geppert JJ, Moos RH. Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891895.
  17. Tomasson K, Vaglum P. The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129136.
  18. Carrier E, McNeely J, Lobach I, Tay S, Gourevitch MN, Raven MC. Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116122.
  19. Sullivan JT, Sykora K, Schneiderman J, Naranjo CA, Sellers EM. Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):13531357.
  20. Hecksel KA, Bostwick JM, Jaeger TM, Cha SS. Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274279.
  21. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  22. Ponzer S, Johansson S‐E, Bergman B. A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):8388.
  23. Walker RD, Howard MO, Anderson B, et al. Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932937.
  24. Regier DA, Farmer ME, Rae DS, et al. Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):25112518.
  25. Greenfield SF, Sugarman DE, Muenz LR, Patterson MD, He DY, Weiss RD. The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):12781285.
  26. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830842.
  27. Shih M, Simon PA. Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521528.
  28. Saitz R, Horton NJ, Larson MJ, Winter M, Samet JH. Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):7078.
  29. Mertens JR, Flisher AJ, Satre DD, Weisner CM. The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):4553.
Issue
Journal of Hospital Medicine - 7(8)
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Journal of Hospital Medicine - 7(8)
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Multiple admissions for alcohol withdrawal
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Evolving Practice of Hospital Medicine

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Evolving practice of hospital medicine and its impact on hospital throughput and efficiencies

Hospitalists are physicians whose primary focus is the general medical care of hospitalized patients. Hospitalists are uniquely positioned to implement strategies to improve patient flow and efficiency.1 With emergency department (ED) diversion reaching rates upward of 70%, lack of access to inpatient beds leads to delayed care with worsened outcomes.25

To improve access to hospital beds, hospitals may increase capacity by either adding beds or by more efficiently using existing beds. Operations management principles have been applied to healthcare to ensure efficient use of beds. These include: reducing variability of scheduled admissions, remeasuring length of stay (LOS) and bed demand after implementing strategies to reduce practice variation, and employing queuing theory to generate predictions of optimal beds needed.6 The Joint Commission implemented a leadership standard (LD 04.03.11) that hospitals develop and implement plans to identify and mitigate impediments to efficient patient flow through the hospital.

To improve access, hospital leaders expect hospitalists to staff in inpatient medicine programs, surgical comanagement, short stay and chest pain units, and active bed management.7 In the following review, we define hospitalists' roles in the aforementioned programs and their effect on patient flow. We also touch on preoperative clinics, palliative care, geographic rounding, and flexible staffing models.

ACUTE INPATIENT CARE

Hospitalists are one of the fastest growing physician groups in the United States.810 Hospitalists improve efficiency and quality of care across a variety of demographic, geographic, and healthcare settings.11, 12 A 2002 retrospective cohort study in a community‐based urban teaching hospital showed that hospitalists decreased LOS by 0.61 days and lowered risk for death in the hospital (adjusted relative hazard, 0.71; 95% confidence interval [CI], 0.540.93).13 A 2004 prospective quasi‐experimental observational study done at an academic teaching hospital showed an adjusted LOS that was 16.2% lower, and adjusted cost 9.7% lower, for patients on the hospitalists' service.14 In 2007, Lindenauer and colleagues found that a national sample of hospitalists decreased LOS by 0.4 days and lowered cost by $286 per patient.15 The findings of these individual studies were supported in a 2009 systematic review of 33 studies by Peterson which showed that hospitalists decrease LOS.16 In a recent study, Kuo and Goodwin showed that while hospitalists decrease LOS and cost, the patients they care for have higher Medicare costs after discharge by $322 per patient, and are more likely to be readmitted (odds ratio, 1.08; CI, 1.041.14).17

The hospitalist model of care continues to grow, and hospitalists will soon number as many as 30,000.18 For acute medical inpatients, the evidence suggests that hospitalists improve patient flow by decreasing LOS while improving other aspects of quality of care. However, Kuo and Goodwin's findings suggest that the transition of care from inpatient to outpatient settings still requires attention.17

SURGICAL COMANAGEMENT

The Society of Hospital Medicine (SHM) core competencies include perioperative medicine.19, 20 In the 2006 SHM national survey, 85% of hospital medicine groups indicated that they participated in surgical comanagement.21

Hospitalists have improved patient flow and outcomes for orthopedic patients. Hospitalist management of hip fracture patients decreases time to surgery and LOS compared to standard care.2224 Phy and colleagues studied 466 patients for 2 years after the inception of hospital medicine comanagement of surgical patients, and found that care by hospitalists decreased LOS by 2.2 days.22 In a retrospective study of 118 patients, Roy and colleagues found that hospitalist‐managed patients had shorter time to consultation and surgery, decreased LOS, and lower costs.23 In a retrospective cohort study, Batsis looked at mortality in 466 patients with hip fracture, and found no difference between hospitalist management and standard care.24 In patients undergoing elective hip and knee arthroplasty, Huddleston and colleagues reported that patients managed by hospitalists had fewer complications and shorter LOS. The nurses and orthopedic surgeons preferred the hospitalistorthopedist comanagement model.25

The benefits of hospitalist comanagement are not limited to adult patients undergoing orthopedic surgery. For high‐risk patients undergoing lower extremity reconstruction surgery, Pinzur and colleagues noted that LOS was shorter for a cohort of patients managed by hospitalists than for a group of historical controls not treated by hospitalists.26 Simon and colleagues studied comanagement for pediatric spinal fusion patients, and found a decrease in LOS from 6.5 to 4.8 days.27

Several factors should be considered in developing and implementing a successful comanagement program. Since comanagement duties may fall upon hospitalists in order to protect surgeons' time,28 hospital medicine groups should ensure adequate staffing prior to taking on additional services. Clear guidelines to delineate roles and responsibilities of the comanaging groups also need to be developed and implemented.29, 30

Comanaging may also involve additional training. Hospitalists who manage neurologic, neurosurgical, trauma, and psychiatric patients report being undertrained for such conditions.31, 32 Hospital medicine groups need to ensure training needs are met and supported. Given the successes of comanagement and the increasing complexity of surgical patients,33 this practice will likely expand to a greater variety of non‐medical patients.

SHORT STAY UNITS

In 2003, short stay units (SSU) were present in approximately 20% of US hospitals, with 11% of hospitals planning on opening one in the next year.34 SSU are designed to manage acute, self‐limited medical conditions that require brief staysusually less than 72 hours. Approximately 80% of SSU patients are discharged home, avoiding hospitalization.35 Historically, SSU have been under the domain of the ED; however, there is an emerging role for hospitalist‐run SSU.36

Despite demand for SSU, little research has been performed on hospitalist‐led SSU. In 2000, Abenhaim and colleagues showed that a hospitalist‐run SSU at a university‐affiliated teaching hospital had a shorter LOS and lower rates of complications and readmissions when compared to medicine teaching services.37 In 2008, Northwestern Memorial Hospital opened a 30‐bed hospitalist‐run SSU; for those patients, LOS decreased by 2 days.38 In 2010, Leykum and colleagues showed that a hospitalist‐run observation unit can decrease LOS from 2.4 days to 2.2 days.39 Careful selection of SSU patients is needed to obtain these results. Lucas and colleagues found that whether or not SSU patients required assistance of specialists was the strongest predictor of unsuccessful stays (>72 hours or inpatient conversion) in SSU.36

Whether SSU are run by hospital medicine or emergency medicine is decided at an institutional level. Location of SSU in a specifically designated area is crucial, as it allows physicians to round efficiently on patients and to work with staff trained in observation services. Development of admission criteria that include specific diagnoses which match hospitalists' scope of practice is also important (Table 1).32

Examples of Conditions Appropriate for Short Stay Unit
Evaluation of Diagnostic Syndromes Treatment of Emergent Conditions
  • NOTE: Adapted from SHM White Paper: Observation Unit White Paper.35

Chest pain Asthma
Abdominal pain Congestive heart failure
Fever Dehydration
Gastrointestinal bleed Hypoglycemia or hyperglycemia
Syncope Hypercalcemia
Dizziness Atrial fibrillation
Headache
Chest trauma
Abdominal trauma

The protocol‐based and diagnosis‐specific nature of SSU may enhance quality of care through standardization. Future research may delineate the utility of SSU.

CHEST PAIN UNITS

In the United States, in 2004, approximately 6 million patients present annually to EDs with chest pain.40 Cost of care of patients unnecessarily admitted to coronary care units has been estimated to be nearly $3 billion annually.41 Still, as many as 3% of patients with acute myocardial infarction are discharged home.42 Chest pain units (CPU) were developed to facilitate evaluation of patients with chest pain, at low risk for acute coronary syndrome, without requiring inpatient admission. A number of studies have suggested that admission to a CPU is a safe and cost‐effective alternative to hospital admission.4348

CPU have traditionally been staffed by ED physicians and/or cardiologists. In a prepost study, Krantz and colleagues found that a CPU model, incorporating hospitalists at an academic public safety‐net hospital, decreased ED LOS with no difference in 30‐day cardiac event rate.49 Myers and colleagues created a hospitalist‐directed nonteaching service in an academic medical center to admit low‐risk chest pain patients. Patients admitted to the hospitalist service had a statistically significant lower median LOS (23 hours vs 33 hours) and approximately half the median hospital charges than those admitted to teaching services.50 At the same academic medical center, Bayley and colleagues showed that 91% of patients admitted for chest pain waited more than 3 hours for a bed. This adversely affected ED revenue by tying up beds, resulting in an estimated annual loss of $168,300 of hospital revenue. Creation of a hospitalist‐managed service for low‐acuity chest pain patients reduced hospital LOS by 7 hours.51 Somekh and colleagues demonstrated that a protocol‐driven, cardiologist‐run CPU results in a decreased LOS and readmission rate compared to usual care.52 In a non‐peer reviewed case study, Cox Health opened an 8‐bed, hospitalist‐led CPU in 2003. They decreased LOS from 72 to 18 hours, while increasing revenue by $2.5 million a year.53 These studies suggest that hospitalist‐run CPU can decrease LOS, increase revenue, and relieve ED overcrowding.

Development of a successful CPU depends upon clear inclusion/exclusion criteria; close collaboration among ED physicians, hospitalists, and cardiologists; the development of evidence‐based protocols, and the availability of stress testing.

ACTIVE BED MANAGEMENT

As of 2007, 90% of EDs were crowded beyond their capacity.2 ED crowding leads to ambulance diversion,54 which can delay care and increase mortality rates.55 One of the main causes of ED crowding is the boarding of admitted patients.56 Boarded, admitted patients have been shown to have decreased quality of care and patient satisfaction.35

Active bed management (ABM) by hospitalists can decrease ED diversion. Howell and colleagues instituted ABM where hospitalists, as active bed managers, facilitate placement of patients to their inpatient destinations to assist ED flow.57 This 24‐hour, hospitalist‐led, active bed management service decreased both ED LOS and ambulance diversion. The bed manager collaborated real‐time with medicine and ED attending physicians, nursing supervisors, and charge nurses to change patient care status, and assign and facilitate transfer of patients to appropriate units. These hospitalist bed managers were also empowered to activate additional resources when pre‐diversion rounds identified resource limitations and impending ED divert. They found overall ED LOS for admitted patients decreased by 98 minutes, while LOS for non‐admitted patients stayed the same. AMB decreased diversion due to critically ill and telemetry patients by 28% (786 hours), and diversion due to lower acuity patients by 6% (182 hours). This intervention proved cost‐effective. Three full‐time equivalent (FTE) hospitalists' salaries staff 1 active bed manager working 24/7. Nearly 1000 hours of diversion were avoided at an annual savings of $1086 per hour of diversion decreased.

ABM is a new frontier for hospitals in general, and hospitalists in particular. Chadaga and colleagues found that a hospital medicine‐ED team participating in active bed management, while caring for admitted patients boarded in the ED, can decrease ED diversion and improve patient flow. The percentage of patients transferred to a medicine floor and discharged within 8 hours was reduced by 67% (P < 0.01), while the number of discharges from the ED of admitted medicine patients increased by 61% (P < 0.001).58

To decrease initial investment, components of ABM (ED triage, bed assignment, discharge facilitation) can be instituted in parts. Hospital medicine groups with limited resources may only provide a triage service by phone for difficult ED cases. Bedside evaluations and collaboration with nursing staff to improve bed placement may be a next step, with floor and/or intensive care unit (ICU) rounds to facilitate early discharges as a final component.

OTHER AREAS

Preoperative Clinics

In 2005, SHM cited preoperative clinics as an important aspect of preoperative care.59 Sehgal and Wachter included preoperative clinics as an area for expanding the role of hospitalists in the United States.60 These clinics can decrease delays to surgery, LOS, and cancellations on the day of surgery.61 The Cleveland Clinic established the Internal Medicine Preoperative Assessment, Consultation, and Treatment (IMPACT) Center in 1997, and has decreased surgery delay rate by 49%.59 At Kaiser Bellflower Medical Center, a preoperative medicine service that provides preoperative screening decreased the number of surgical procedures cancelled on the day of surgery by more than half.62 Gates Hospitalists LLC's perioperative care decreased delay to surgery and lost operating room time.63 In order for a preoperative service to be successful, there must be buy‐in from hospitalists, surgeons, and primary care physicians, as well as adequate staffing and clinical support.59

Palliative Care

Palliative care has been identified by SHM as a core competency in hospital medicine.64 There are several key components in delivery of quality palliative care, including communication about prognosis, pain and symptom control, and hospice eligibility.65 Hospitalists are in a unique position to offer and improve palliative care for hospitalized patients. The majority of hospitalists report spending significant amounts of time caring for dying patients; thereby, hospitalists frequently provide end‐of‐life care.66, 67 Compared to community‐based physicians, patients cared for by hospitalists have higher odds of having documented family discussions regarding end‐of‐life care, and have fewer or no key symptoms (pain, anxiety, or dyspnea).66 In addition, hospitalists' availability improves response time when a patient's clinical status changes or deteriorates, leading to prompter delivery of symptom alleviation.65 Hospitalists are becoming more experienced with end‐of‐life care, as they are exposed to terminally ill patients on a daily basis. More experience leads to improved recognition of patients with limited prognosis, which leads to earlier discussions about goals of care and faster delivery of palliative care. Perhaps this could decrease LOS and be a future area of study.

Geographic Rounding

In the last 5 years, hospital administrators have promoted geographic rounding, where hospitalists see all their patients in 1 geographic location.69 The driving forces behind this include poor patient satisfaction with physician availability, large amounts of time spent by hospitalists in transit to and from patient locations, and frustrations regarding communication with nursing.70 Several groups have instituted this with success. Cleveland Clinic and Virtua Memorial Hospital have found improved patient satisfaction and decreased LOS.69, 70 O'Leary and colleagues found improved awareness of care plans by the entire team.71 Caution should be taken to assure proper physician‐to‐patient ratios, avoid physician isolation, and coordinate physician shifts with bed assignments.69 To address some of these issues, groups have used a hybrid model where a hospitalist is primarily located on one unit but can flex or overflow onto another unit.70 Steps to success with geographic rounding include buy‐in from the institution and nursing, assuring a safe physician‐to‐patient ratio, avoiding wasted beds, and facilitating multidisciplinary rounds.69

Flexible Staffing Models

In SHM's 2010 State of Hospital Medicine Report, 70% of hospitalist groups used a fixed shift‐based staffing model (ie, 7 days on/7 days off).72 Flexible staffing models in which physician coverage is adjusted to patient volume are growing in popularity. This model can be tailored for each institution by examining admission and patient volume trends to increase coverage during busy periods and decrease coverage during slower periods. Potential benefits include alleviating burn out, reducing LOS, and improving patient outcomes. Nursing data suggests that a higher patient‐to‐nursing ratio is associated with increased 30‐day mortality,73 and an ED study found that increasing physician coverage during the evening shift shortened ED LOS by 20%.74 To date, none of these endpoints have been studied for hospital medicine.

CONCLUSION

While many hospital medicine groups were started to provide acute inpatient medical care, most have found that their value to hospitals reaches beyond bedside care. With an epidemic of ED diversion and lack of access to hospital beds and services, optimizing throughput has become imperative for hospital systems. While hospital access can be improved with addition of new beds, improving throughput by decreasing LOS maximizes utilization of existing resources.

We have reviewed how hospitalists improve patient flow in acute inpatient medicine, surgical comanagement, short stay units, chest pain units, and active bed management. In each instance, the literature supports measures for decreasing LOS while maintaining or improving quality of care. Hinami and colleagues showed physician satisfaction with hospitalist‐provided patient care.75 Most studies have been limited by tracking upstream effects of improved efficiency. As there is now some evidence that decreasing LOS may increase readmissions,17 future studies should incorporate this metric into their outcomes. The effect of formal operations management principles on patient flow and bed efficiency is not well known and should be further examined.

In addition, we have touched on other areas (perioperative clinics, palliative care, geographic rounding, and flexible staffing models) where hospitalists may impact patient throughput. These areas represent excellent opportunities for future research.

Hospitalist participation in many of these areas is in its infancy. Hospital medicine programs interested in expanding their services, beyond acute inpatient care, have the opportunity to develop standards and continue research on the effect of hospital medicine‐led services on patient care and flow.

Acknowledgements

Disclosure: All authors disclose no relevant or financial conflicts of interest.

Files
References
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Journal of Hospital Medicine - 7(8)
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649-654
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Hospitalists are physicians whose primary focus is the general medical care of hospitalized patients. Hospitalists are uniquely positioned to implement strategies to improve patient flow and efficiency.1 With emergency department (ED) diversion reaching rates upward of 70%, lack of access to inpatient beds leads to delayed care with worsened outcomes.25

To improve access to hospital beds, hospitals may increase capacity by either adding beds or by more efficiently using existing beds. Operations management principles have been applied to healthcare to ensure efficient use of beds. These include: reducing variability of scheduled admissions, remeasuring length of stay (LOS) and bed demand after implementing strategies to reduce practice variation, and employing queuing theory to generate predictions of optimal beds needed.6 The Joint Commission implemented a leadership standard (LD 04.03.11) that hospitals develop and implement plans to identify and mitigate impediments to efficient patient flow through the hospital.

To improve access, hospital leaders expect hospitalists to staff in inpatient medicine programs, surgical comanagement, short stay and chest pain units, and active bed management.7 In the following review, we define hospitalists' roles in the aforementioned programs and their effect on patient flow. We also touch on preoperative clinics, palliative care, geographic rounding, and flexible staffing models.

ACUTE INPATIENT CARE

Hospitalists are one of the fastest growing physician groups in the United States.810 Hospitalists improve efficiency and quality of care across a variety of demographic, geographic, and healthcare settings.11, 12 A 2002 retrospective cohort study in a community‐based urban teaching hospital showed that hospitalists decreased LOS by 0.61 days and lowered risk for death in the hospital (adjusted relative hazard, 0.71; 95% confidence interval [CI], 0.540.93).13 A 2004 prospective quasi‐experimental observational study done at an academic teaching hospital showed an adjusted LOS that was 16.2% lower, and adjusted cost 9.7% lower, for patients on the hospitalists' service.14 In 2007, Lindenauer and colleagues found that a national sample of hospitalists decreased LOS by 0.4 days and lowered cost by $286 per patient.15 The findings of these individual studies were supported in a 2009 systematic review of 33 studies by Peterson which showed that hospitalists decrease LOS.16 In a recent study, Kuo and Goodwin showed that while hospitalists decrease LOS and cost, the patients they care for have higher Medicare costs after discharge by $322 per patient, and are more likely to be readmitted (odds ratio, 1.08; CI, 1.041.14).17

The hospitalist model of care continues to grow, and hospitalists will soon number as many as 30,000.18 For acute medical inpatients, the evidence suggests that hospitalists improve patient flow by decreasing LOS while improving other aspects of quality of care. However, Kuo and Goodwin's findings suggest that the transition of care from inpatient to outpatient settings still requires attention.17

SURGICAL COMANAGEMENT

The Society of Hospital Medicine (SHM) core competencies include perioperative medicine.19, 20 In the 2006 SHM national survey, 85% of hospital medicine groups indicated that they participated in surgical comanagement.21

Hospitalists have improved patient flow and outcomes for orthopedic patients. Hospitalist management of hip fracture patients decreases time to surgery and LOS compared to standard care.2224 Phy and colleagues studied 466 patients for 2 years after the inception of hospital medicine comanagement of surgical patients, and found that care by hospitalists decreased LOS by 2.2 days.22 In a retrospective study of 118 patients, Roy and colleagues found that hospitalist‐managed patients had shorter time to consultation and surgery, decreased LOS, and lower costs.23 In a retrospective cohort study, Batsis looked at mortality in 466 patients with hip fracture, and found no difference between hospitalist management and standard care.24 In patients undergoing elective hip and knee arthroplasty, Huddleston and colleagues reported that patients managed by hospitalists had fewer complications and shorter LOS. The nurses and orthopedic surgeons preferred the hospitalistorthopedist comanagement model.25

The benefits of hospitalist comanagement are not limited to adult patients undergoing orthopedic surgery. For high‐risk patients undergoing lower extremity reconstruction surgery, Pinzur and colleagues noted that LOS was shorter for a cohort of patients managed by hospitalists than for a group of historical controls not treated by hospitalists.26 Simon and colleagues studied comanagement for pediatric spinal fusion patients, and found a decrease in LOS from 6.5 to 4.8 days.27

Several factors should be considered in developing and implementing a successful comanagement program. Since comanagement duties may fall upon hospitalists in order to protect surgeons' time,28 hospital medicine groups should ensure adequate staffing prior to taking on additional services. Clear guidelines to delineate roles and responsibilities of the comanaging groups also need to be developed and implemented.29, 30

Comanaging may also involve additional training. Hospitalists who manage neurologic, neurosurgical, trauma, and psychiatric patients report being undertrained for such conditions.31, 32 Hospital medicine groups need to ensure training needs are met and supported. Given the successes of comanagement and the increasing complexity of surgical patients,33 this practice will likely expand to a greater variety of non‐medical patients.

SHORT STAY UNITS

In 2003, short stay units (SSU) were present in approximately 20% of US hospitals, with 11% of hospitals planning on opening one in the next year.34 SSU are designed to manage acute, self‐limited medical conditions that require brief staysusually less than 72 hours. Approximately 80% of SSU patients are discharged home, avoiding hospitalization.35 Historically, SSU have been under the domain of the ED; however, there is an emerging role for hospitalist‐run SSU.36

Despite demand for SSU, little research has been performed on hospitalist‐led SSU. In 2000, Abenhaim and colleagues showed that a hospitalist‐run SSU at a university‐affiliated teaching hospital had a shorter LOS and lower rates of complications and readmissions when compared to medicine teaching services.37 In 2008, Northwestern Memorial Hospital opened a 30‐bed hospitalist‐run SSU; for those patients, LOS decreased by 2 days.38 In 2010, Leykum and colleagues showed that a hospitalist‐run observation unit can decrease LOS from 2.4 days to 2.2 days.39 Careful selection of SSU patients is needed to obtain these results. Lucas and colleagues found that whether or not SSU patients required assistance of specialists was the strongest predictor of unsuccessful stays (>72 hours or inpatient conversion) in SSU.36

Whether SSU are run by hospital medicine or emergency medicine is decided at an institutional level. Location of SSU in a specifically designated area is crucial, as it allows physicians to round efficiently on patients and to work with staff trained in observation services. Development of admission criteria that include specific diagnoses which match hospitalists' scope of practice is also important (Table 1).32

Examples of Conditions Appropriate for Short Stay Unit
Evaluation of Diagnostic Syndromes Treatment of Emergent Conditions
  • NOTE: Adapted from SHM White Paper: Observation Unit White Paper.35

Chest pain Asthma
Abdominal pain Congestive heart failure
Fever Dehydration
Gastrointestinal bleed Hypoglycemia or hyperglycemia
Syncope Hypercalcemia
Dizziness Atrial fibrillation
Headache
Chest trauma
Abdominal trauma

The protocol‐based and diagnosis‐specific nature of SSU may enhance quality of care through standardization. Future research may delineate the utility of SSU.

CHEST PAIN UNITS

In the United States, in 2004, approximately 6 million patients present annually to EDs with chest pain.40 Cost of care of patients unnecessarily admitted to coronary care units has been estimated to be nearly $3 billion annually.41 Still, as many as 3% of patients with acute myocardial infarction are discharged home.42 Chest pain units (CPU) were developed to facilitate evaluation of patients with chest pain, at low risk for acute coronary syndrome, without requiring inpatient admission. A number of studies have suggested that admission to a CPU is a safe and cost‐effective alternative to hospital admission.4348

CPU have traditionally been staffed by ED physicians and/or cardiologists. In a prepost study, Krantz and colleagues found that a CPU model, incorporating hospitalists at an academic public safety‐net hospital, decreased ED LOS with no difference in 30‐day cardiac event rate.49 Myers and colleagues created a hospitalist‐directed nonteaching service in an academic medical center to admit low‐risk chest pain patients. Patients admitted to the hospitalist service had a statistically significant lower median LOS (23 hours vs 33 hours) and approximately half the median hospital charges than those admitted to teaching services.50 At the same academic medical center, Bayley and colleagues showed that 91% of patients admitted for chest pain waited more than 3 hours for a bed. This adversely affected ED revenue by tying up beds, resulting in an estimated annual loss of $168,300 of hospital revenue. Creation of a hospitalist‐managed service for low‐acuity chest pain patients reduced hospital LOS by 7 hours.51 Somekh and colleagues demonstrated that a protocol‐driven, cardiologist‐run CPU results in a decreased LOS and readmission rate compared to usual care.52 In a non‐peer reviewed case study, Cox Health opened an 8‐bed, hospitalist‐led CPU in 2003. They decreased LOS from 72 to 18 hours, while increasing revenue by $2.5 million a year.53 These studies suggest that hospitalist‐run CPU can decrease LOS, increase revenue, and relieve ED overcrowding.

Development of a successful CPU depends upon clear inclusion/exclusion criteria; close collaboration among ED physicians, hospitalists, and cardiologists; the development of evidence‐based protocols, and the availability of stress testing.

ACTIVE BED MANAGEMENT

As of 2007, 90% of EDs were crowded beyond their capacity.2 ED crowding leads to ambulance diversion,54 which can delay care and increase mortality rates.55 One of the main causes of ED crowding is the boarding of admitted patients.56 Boarded, admitted patients have been shown to have decreased quality of care and patient satisfaction.35

Active bed management (ABM) by hospitalists can decrease ED diversion. Howell and colleagues instituted ABM where hospitalists, as active bed managers, facilitate placement of patients to their inpatient destinations to assist ED flow.57 This 24‐hour, hospitalist‐led, active bed management service decreased both ED LOS and ambulance diversion. The bed manager collaborated real‐time with medicine and ED attending physicians, nursing supervisors, and charge nurses to change patient care status, and assign and facilitate transfer of patients to appropriate units. These hospitalist bed managers were also empowered to activate additional resources when pre‐diversion rounds identified resource limitations and impending ED divert. They found overall ED LOS for admitted patients decreased by 98 minutes, while LOS for non‐admitted patients stayed the same. AMB decreased diversion due to critically ill and telemetry patients by 28% (786 hours), and diversion due to lower acuity patients by 6% (182 hours). This intervention proved cost‐effective. Three full‐time equivalent (FTE) hospitalists' salaries staff 1 active bed manager working 24/7. Nearly 1000 hours of diversion were avoided at an annual savings of $1086 per hour of diversion decreased.

ABM is a new frontier for hospitals in general, and hospitalists in particular. Chadaga and colleagues found that a hospital medicine‐ED team participating in active bed management, while caring for admitted patients boarded in the ED, can decrease ED diversion and improve patient flow. The percentage of patients transferred to a medicine floor and discharged within 8 hours was reduced by 67% (P < 0.01), while the number of discharges from the ED of admitted medicine patients increased by 61% (P < 0.001).58

To decrease initial investment, components of ABM (ED triage, bed assignment, discharge facilitation) can be instituted in parts. Hospital medicine groups with limited resources may only provide a triage service by phone for difficult ED cases. Bedside evaluations and collaboration with nursing staff to improve bed placement may be a next step, with floor and/or intensive care unit (ICU) rounds to facilitate early discharges as a final component.

OTHER AREAS

Preoperative Clinics

In 2005, SHM cited preoperative clinics as an important aspect of preoperative care.59 Sehgal and Wachter included preoperative clinics as an area for expanding the role of hospitalists in the United States.60 These clinics can decrease delays to surgery, LOS, and cancellations on the day of surgery.61 The Cleveland Clinic established the Internal Medicine Preoperative Assessment, Consultation, and Treatment (IMPACT) Center in 1997, and has decreased surgery delay rate by 49%.59 At Kaiser Bellflower Medical Center, a preoperative medicine service that provides preoperative screening decreased the number of surgical procedures cancelled on the day of surgery by more than half.62 Gates Hospitalists LLC's perioperative care decreased delay to surgery and lost operating room time.63 In order for a preoperative service to be successful, there must be buy‐in from hospitalists, surgeons, and primary care physicians, as well as adequate staffing and clinical support.59

Palliative Care

Palliative care has been identified by SHM as a core competency in hospital medicine.64 There are several key components in delivery of quality palliative care, including communication about prognosis, pain and symptom control, and hospice eligibility.65 Hospitalists are in a unique position to offer and improve palliative care for hospitalized patients. The majority of hospitalists report spending significant amounts of time caring for dying patients; thereby, hospitalists frequently provide end‐of‐life care.66, 67 Compared to community‐based physicians, patients cared for by hospitalists have higher odds of having documented family discussions regarding end‐of‐life care, and have fewer or no key symptoms (pain, anxiety, or dyspnea).66 In addition, hospitalists' availability improves response time when a patient's clinical status changes or deteriorates, leading to prompter delivery of symptom alleviation.65 Hospitalists are becoming more experienced with end‐of‐life care, as they are exposed to terminally ill patients on a daily basis. More experience leads to improved recognition of patients with limited prognosis, which leads to earlier discussions about goals of care and faster delivery of palliative care. Perhaps this could decrease LOS and be a future area of study.

Geographic Rounding

In the last 5 years, hospital administrators have promoted geographic rounding, where hospitalists see all their patients in 1 geographic location.69 The driving forces behind this include poor patient satisfaction with physician availability, large amounts of time spent by hospitalists in transit to and from patient locations, and frustrations regarding communication with nursing.70 Several groups have instituted this with success. Cleveland Clinic and Virtua Memorial Hospital have found improved patient satisfaction and decreased LOS.69, 70 O'Leary and colleagues found improved awareness of care plans by the entire team.71 Caution should be taken to assure proper physician‐to‐patient ratios, avoid physician isolation, and coordinate physician shifts with bed assignments.69 To address some of these issues, groups have used a hybrid model where a hospitalist is primarily located on one unit but can flex or overflow onto another unit.70 Steps to success with geographic rounding include buy‐in from the institution and nursing, assuring a safe physician‐to‐patient ratio, avoiding wasted beds, and facilitating multidisciplinary rounds.69

Flexible Staffing Models

In SHM's 2010 State of Hospital Medicine Report, 70% of hospitalist groups used a fixed shift‐based staffing model (ie, 7 days on/7 days off).72 Flexible staffing models in which physician coverage is adjusted to patient volume are growing in popularity. This model can be tailored for each institution by examining admission and patient volume trends to increase coverage during busy periods and decrease coverage during slower periods. Potential benefits include alleviating burn out, reducing LOS, and improving patient outcomes. Nursing data suggests that a higher patient‐to‐nursing ratio is associated with increased 30‐day mortality,73 and an ED study found that increasing physician coverage during the evening shift shortened ED LOS by 20%.74 To date, none of these endpoints have been studied for hospital medicine.

CONCLUSION

While many hospital medicine groups were started to provide acute inpatient medical care, most have found that their value to hospitals reaches beyond bedside care. With an epidemic of ED diversion and lack of access to hospital beds and services, optimizing throughput has become imperative for hospital systems. While hospital access can be improved with addition of new beds, improving throughput by decreasing LOS maximizes utilization of existing resources.

We have reviewed how hospitalists improve patient flow in acute inpatient medicine, surgical comanagement, short stay units, chest pain units, and active bed management. In each instance, the literature supports measures for decreasing LOS while maintaining or improving quality of care. Hinami and colleagues showed physician satisfaction with hospitalist‐provided patient care.75 Most studies have been limited by tracking upstream effects of improved efficiency. As there is now some evidence that decreasing LOS may increase readmissions,17 future studies should incorporate this metric into their outcomes. The effect of formal operations management principles on patient flow and bed efficiency is not well known and should be further examined.

In addition, we have touched on other areas (perioperative clinics, palliative care, geographic rounding, and flexible staffing models) where hospitalists may impact patient throughput. These areas represent excellent opportunities for future research.

Hospitalist participation in many of these areas is in its infancy. Hospital medicine programs interested in expanding their services, beyond acute inpatient care, have the opportunity to develop standards and continue research on the effect of hospital medicine‐led services on patient care and flow.

Acknowledgements

Disclosure: All authors disclose no relevant or financial conflicts of interest.

Hospitalists are physicians whose primary focus is the general medical care of hospitalized patients. Hospitalists are uniquely positioned to implement strategies to improve patient flow and efficiency.1 With emergency department (ED) diversion reaching rates upward of 70%, lack of access to inpatient beds leads to delayed care with worsened outcomes.25

To improve access to hospital beds, hospitals may increase capacity by either adding beds or by more efficiently using existing beds. Operations management principles have been applied to healthcare to ensure efficient use of beds. These include: reducing variability of scheduled admissions, remeasuring length of stay (LOS) and bed demand after implementing strategies to reduce practice variation, and employing queuing theory to generate predictions of optimal beds needed.6 The Joint Commission implemented a leadership standard (LD 04.03.11) that hospitals develop and implement plans to identify and mitigate impediments to efficient patient flow through the hospital.

To improve access, hospital leaders expect hospitalists to staff in inpatient medicine programs, surgical comanagement, short stay and chest pain units, and active bed management.7 In the following review, we define hospitalists' roles in the aforementioned programs and their effect on patient flow. We also touch on preoperative clinics, palliative care, geographic rounding, and flexible staffing models.

ACUTE INPATIENT CARE

Hospitalists are one of the fastest growing physician groups in the United States.810 Hospitalists improve efficiency and quality of care across a variety of demographic, geographic, and healthcare settings.11, 12 A 2002 retrospective cohort study in a community‐based urban teaching hospital showed that hospitalists decreased LOS by 0.61 days and lowered risk for death in the hospital (adjusted relative hazard, 0.71; 95% confidence interval [CI], 0.540.93).13 A 2004 prospective quasi‐experimental observational study done at an academic teaching hospital showed an adjusted LOS that was 16.2% lower, and adjusted cost 9.7% lower, for patients on the hospitalists' service.14 In 2007, Lindenauer and colleagues found that a national sample of hospitalists decreased LOS by 0.4 days and lowered cost by $286 per patient.15 The findings of these individual studies were supported in a 2009 systematic review of 33 studies by Peterson which showed that hospitalists decrease LOS.16 In a recent study, Kuo and Goodwin showed that while hospitalists decrease LOS and cost, the patients they care for have higher Medicare costs after discharge by $322 per patient, and are more likely to be readmitted (odds ratio, 1.08; CI, 1.041.14).17

The hospitalist model of care continues to grow, and hospitalists will soon number as many as 30,000.18 For acute medical inpatients, the evidence suggests that hospitalists improve patient flow by decreasing LOS while improving other aspects of quality of care. However, Kuo and Goodwin's findings suggest that the transition of care from inpatient to outpatient settings still requires attention.17

SURGICAL COMANAGEMENT

The Society of Hospital Medicine (SHM) core competencies include perioperative medicine.19, 20 In the 2006 SHM national survey, 85% of hospital medicine groups indicated that they participated in surgical comanagement.21

Hospitalists have improved patient flow and outcomes for orthopedic patients. Hospitalist management of hip fracture patients decreases time to surgery and LOS compared to standard care.2224 Phy and colleagues studied 466 patients for 2 years after the inception of hospital medicine comanagement of surgical patients, and found that care by hospitalists decreased LOS by 2.2 days.22 In a retrospective study of 118 patients, Roy and colleagues found that hospitalist‐managed patients had shorter time to consultation and surgery, decreased LOS, and lower costs.23 In a retrospective cohort study, Batsis looked at mortality in 466 patients with hip fracture, and found no difference between hospitalist management and standard care.24 In patients undergoing elective hip and knee arthroplasty, Huddleston and colleagues reported that patients managed by hospitalists had fewer complications and shorter LOS. The nurses and orthopedic surgeons preferred the hospitalistorthopedist comanagement model.25

The benefits of hospitalist comanagement are not limited to adult patients undergoing orthopedic surgery. For high‐risk patients undergoing lower extremity reconstruction surgery, Pinzur and colleagues noted that LOS was shorter for a cohort of patients managed by hospitalists than for a group of historical controls not treated by hospitalists.26 Simon and colleagues studied comanagement for pediatric spinal fusion patients, and found a decrease in LOS from 6.5 to 4.8 days.27

Several factors should be considered in developing and implementing a successful comanagement program. Since comanagement duties may fall upon hospitalists in order to protect surgeons' time,28 hospital medicine groups should ensure adequate staffing prior to taking on additional services. Clear guidelines to delineate roles and responsibilities of the comanaging groups also need to be developed and implemented.29, 30

Comanaging may also involve additional training. Hospitalists who manage neurologic, neurosurgical, trauma, and psychiatric patients report being undertrained for such conditions.31, 32 Hospital medicine groups need to ensure training needs are met and supported. Given the successes of comanagement and the increasing complexity of surgical patients,33 this practice will likely expand to a greater variety of non‐medical patients.

SHORT STAY UNITS

In 2003, short stay units (SSU) were present in approximately 20% of US hospitals, with 11% of hospitals planning on opening one in the next year.34 SSU are designed to manage acute, self‐limited medical conditions that require brief staysusually less than 72 hours. Approximately 80% of SSU patients are discharged home, avoiding hospitalization.35 Historically, SSU have been under the domain of the ED; however, there is an emerging role for hospitalist‐run SSU.36

Despite demand for SSU, little research has been performed on hospitalist‐led SSU. In 2000, Abenhaim and colleagues showed that a hospitalist‐run SSU at a university‐affiliated teaching hospital had a shorter LOS and lower rates of complications and readmissions when compared to medicine teaching services.37 In 2008, Northwestern Memorial Hospital opened a 30‐bed hospitalist‐run SSU; for those patients, LOS decreased by 2 days.38 In 2010, Leykum and colleagues showed that a hospitalist‐run observation unit can decrease LOS from 2.4 days to 2.2 days.39 Careful selection of SSU patients is needed to obtain these results. Lucas and colleagues found that whether or not SSU patients required assistance of specialists was the strongest predictor of unsuccessful stays (>72 hours or inpatient conversion) in SSU.36

Whether SSU are run by hospital medicine or emergency medicine is decided at an institutional level. Location of SSU in a specifically designated area is crucial, as it allows physicians to round efficiently on patients and to work with staff trained in observation services. Development of admission criteria that include specific diagnoses which match hospitalists' scope of practice is also important (Table 1).32

Examples of Conditions Appropriate for Short Stay Unit
Evaluation of Diagnostic Syndromes Treatment of Emergent Conditions
  • NOTE: Adapted from SHM White Paper: Observation Unit White Paper.35

Chest pain Asthma
Abdominal pain Congestive heart failure
Fever Dehydration
Gastrointestinal bleed Hypoglycemia or hyperglycemia
Syncope Hypercalcemia
Dizziness Atrial fibrillation
Headache
Chest trauma
Abdominal trauma

The protocol‐based and diagnosis‐specific nature of SSU may enhance quality of care through standardization. Future research may delineate the utility of SSU.

CHEST PAIN UNITS

In the United States, in 2004, approximately 6 million patients present annually to EDs with chest pain.40 Cost of care of patients unnecessarily admitted to coronary care units has been estimated to be nearly $3 billion annually.41 Still, as many as 3% of patients with acute myocardial infarction are discharged home.42 Chest pain units (CPU) were developed to facilitate evaluation of patients with chest pain, at low risk for acute coronary syndrome, without requiring inpatient admission. A number of studies have suggested that admission to a CPU is a safe and cost‐effective alternative to hospital admission.4348

CPU have traditionally been staffed by ED physicians and/or cardiologists. In a prepost study, Krantz and colleagues found that a CPU model, incorporating hospitalists at an academic public safety‐net hospital, decreased ED LOS with no difference in 30‐day cardiac event rate.49 Myers and colleagues created a hospitalist‐directed nonteaching service in an academic medical center to admit low‐risk chest pain patients. Patients admitted to the hospitalist service had a statistically significant lower median LOS (23 hours vs 33 hours) and approximately half the median hospital charges than those admitted to teaching services.50 At the same academic medical center, Bayley and colleagues showed that 91% of patients admitted for chest pain waited more than 3 hours for a bed. This adversely affected ED revenue by tying up beds, resulting in an estimated annual loss of $168,300 of hospital revenue. Creation of a hospitalist‐managed service for low‐acuity chest pain patients reduced hospital LOS by 7 hours.51 Somekh and colleagues demonstrated that a protocol‐driven, cardiologist‐run CPU results in a decreased LOS and readmission rate compared to usual care.52 In a non‐peer reviewed case study, Cox Health opened an 8‐bed, hospitalist‐led CPU in 2003. They decreased LOS from 72 to 18 hours, while increasing revenue by $2.5 million a year.53 These studies suggest that hospitalist‐run CPU can decrease LOS, increase revenue, and relieve ED overcrowding.

Development of a successful CPU depends upon clear inclusion/exclusion criteria; close collaboration among ED physicians, hospitalists, and cardiologists; the development of evidence‐based protocols, and the availability of stress testing.

ACTIVE BED MANAGEMENT

As of 2007, 90% of EDs were crowded beyond their capacity.2 ED crowding leads to ambulance diversion,54 which can delay care and increase mortality rates.55 One of the main causes of ED crowding is the boarding of admitted patients.56 Boarded, admitted patients have been shown to have decreased quality of care and patient satisfaction.35

Active bed management (ABM) by hospitalists can decrease ED diversion. Howell and colleagues instituted ABM where hospitalists, as active bed managers, facilitate placement of patients to their inpatient destinations to assist ED flow.57 This 24‐hour, hospitalist‐led, active bed management service decreased both ED LOS and ambulance diversion. The bed manager collaborated real‐time with medicine and ED attending physicians, nursing supervisors, and charge nurses to change patient care status, and assign and facilitate transfer of patients to appropriate units. These hospitalist bed managers were also empowered to activate additional resources when pre‐diversion rounds identified resource limitations and impending ED divert. They found overall ED LOS for admitted patients decreased by 98 minutes, while LOS for non‐admitted patients stayed the same. AMB decreased diversion due to critically ill and telemetry patients by 28% (786 hours), and diversion due to lower acuity patients by 6% (182 hours). This intervention proved cost‐effective. Three full‐time equivalent (FTE) hospitalists' salaries staff 1 active bed manager working 24/7. Nearly 1000 hours of diversion were avoided at an annual savings of $1086 per hour of diversion decreased.

ABM is a new frontier for hospitals in general, and hospitalists in particular. Chadaga and colleagues found that a hospital medicine‐ED team participating in active bed management, while caring for admitted patients boarded in the ED, can decrease ED diversion and improve patient flow. The percentage of patients transferred to a medicine floor and discharged within 8 hours was reduced by 67% (P < 0.01), while the number of discharges from the ED of admitted medicine patients increased by 61% (P < 0.001).58

To decrease initial investment, components of ABM (ED triage, bed assignment, discharge facilitation) can be instituted in parts. Hospital medicine groups with limited resources may only provide a triage service by phone for difficult ED cases. Bedside evaluations and collaboration with nursing staff to improve bed placement may be a next step, with floor and/or intensive care unit (ICU) rounds to facilitate early discharges as a final component.

OTHER AREAS

Preoperative Clinics

In 2005, SHM cited preoperative clinics as an important aspect of preoperative care.59 Sehgal and Wachter included preoperative clinics as an area for expanding the role of hospitalists in the United States.60 These clinics can decrease delays to surgery, LOS, and cancellations on the day of surgery.61 The Cleveland Clinic established the Internal Medicine Preoperative Assessment, Consultation, and Treatment (IMPACT) Center in 1997, and has decreased surgery delay rate by 49%.59 At Kaiser Bellflower Medical Center, a preoperative medicine service that provides preoperative screening decreased the number of surgical procedures cancelled on the day of surgery by more than half.62 Gates Hospitalists LLC's perioperative care decreased delay to surgery and lost operating room time.63 In order for a preoperative service to be successful, there must be buy‐in from hospitalists, surgeons, and primary care physicians, as well as adequate staffing and clinical support.59

Palliative Care

Palliative care has been identified by SHM as a core competency in hospital medicine.64 There are several key components in delivery of quality palliative care, including communication about prognosis, pain and symptom control, and hospice eligibility.65 Hospitalists are in a unique position to offer and improve palliative care for hospitalized patients. The majority of hospitalists report spending significant amounts of time caring for dying patients; thereby, hospitalists frequently provide end‐of‐life care.66, 67 Compared to community‐based physicians, patients cared for by hospitalists have higher odds of having documented family discussions regarding end‐of‐life care, and have fewer or no key symptoms (pain, anxiety, or dyspnea).66 In addition, hospitalists' availability improves response time when a patient's clinical status changes or deteriorates, leading to prompter delivery of symptom alleviation.65 Hospitalists are becoming more experienced with end‐of‐life care, as they are exposed to terminally ill patients on a daily basis. More experience leads to improved recognition of patients with limited prognosis, which leads to earlier discussions about goals of care and faster delivery of palliative care. Perhaps this could decrease LOS and be a future area of study.

Geographic Rounding

In the last 5 years, hospital administrators have promoted geographic rounding, where hospitalists see all their patients in 1 geographic location.69 The driving forces behind this include poor patient satisfaction with physician availability, large amounts of time spent by hospitalists in transit to and from patient locations, and frustrations regarding communication with nursing.70 Several groups have instituted this with success. Cleveland Clinic and Virtua Memorial Hospital have found improved patient satisfaction and decreased LOS.69, 70 O'Leary and colleagues found improved awareness of care plans by the entire team.71 Caution should be taken to assure proper physician‐to‐patient ratios, avoid physician isolation, and coordinate physician shifts with bed assignments.69 To address some of these issues, groups have used a hybrid model where a hospitalist is primarily located on one unit but can flex or overflow onto another unit.70 Steps to success with geographic rounding include buy‐in from the institution and nursing, assuring a safe physician‐to‐patient ratio, avoiding wasted beds, and facilitating multidisciplinary rounds.69

Flexible Staffing Models

In SHM's 2010 State of Hospital Medicine Report, 70% of hospitalist groups used a fixed shift‐based staffing model (ie, 7 days on/7 days off).72 Flexible staffing models in which physician coverage is adjusted to patient volume are growing in popularity. This model can be tailored for each institution by examining admission and patient volume trends to increase coverage during busy periods and decrease coverage during slower periods. Potential benefits include alleviating burn out, reducing LOS, and improving patient outcomes. Nursing data suggests that a higher patient‐to‐nursing ratio is associated with increased 30‐day mortality,73 and an ED study found that increasing physician coverage during the evening shift shortened ED LOS by 20%.74 To date, none of these endpoints have been studied for hospital medicine.

CONCLUSION

While many hospital medicine groups were started to provide acute inpatient medical care, most have found that their value to hospitals reaches beyond bedside care. With an epidemic of ED diversion and lack of access to hospital beds and services, optimizing throughput has become imperative for hospital systems. While hospital access can be improved with addition of new beds, improving throughput by decreasing LOS maximizes utilization of existing resources.

We have reviewed how hospitalists improve patient flow in acute inpatient medicine, surgical comanagement, short stay units, chest pain units, and active bed management. In each instance, the literature supports measures for decreasing LOS while maintaining or improving quality of care. Hinami and colleagues showed physician satisfaction with hospitalist‐provided patient care.75 Most studies have been limited by tracking upstream effects of improved efficiency. As there is now some evidence that decreasing LOS may increase readmissions,17 future studies should incorporate this metric into their outcomes. The effect of formal operations management principles on patient flow and bed efficiency is not well known and should be further examined.

In addition, we have touched on other areas (perioperative clinics, palliative care, geographic rounding, and flexible staffing models) where hospitalists may impact patient throughput. These areas represent excellent opportunities for future research.

Hospitalist participation in many of these areas is in its infancy. Hospital medicine programs interested in expanding their services, beyond acute inpatient care, have the opportunity to develop standards and continue research on the effect of hospital medicine‐led services on patient care and flow.

Acknowledgements

Disclosure: All authors disclose no relevant or financial conflicts of interest.

References
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  53. Fatovich DM, Nagree Y, Spirvulis P. Access block cause emergency department overcrowding and ambulance diversion in Perth, Western Australia. Emerg Med J. 2005;22:351354.
  54. Nicholl J, West J, Goodacre S, Tuner J. The relationship between distance to hospital and patient mortality in emergencies: an observational study. Emerg Med J. 2007;24:665668.
  55. Hoot N, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52:126136.
  56. Howell E, Bessman E, Kravat S, Kolodner K, Marshall R, Wright S. Active bed management by hospitalists and emergency department throughput. Ann Intern Med. 2008;149:804810.
  57. Chadaga S, Mancini D, Mehler PS, et al. A hospitalist‐led emergency department team improves hospital bed efficiency. J Hosp Med. 2010;5(suppl 1):1718.
  58. Society of Hospital Medicine. Perioperative care (a special supplement to The Hospitalist). Philadelphia, PA: Society of Hospital Medicine; 2005. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Home136:591596.
  59. Hospitalist Management Advisor. Hospitalist branch into preoperative medicine with preop assessments. Marblehead, MA: HCPro, 2006. Available at: http://www.hcpro.com/HOM‐57460–3615/Hospitalists‐branch‐into‐perioperative‐medicine‐with‐preop‐assessments.html. Accessed February 15, 2012.
  60. Magallanes M. The preoperative medicine service: an innovative practice at Kaiser Bellflower Medical Center. The Permanente Journal. 2002;6:1316.
  61. Darves B. A preop evaluation service delivers unexpected benefits. Today's Hospitalist. January 2008.
  62. Pistoria MJ, Amin AN, Dressler DD, McKean SCW, Budnitz TL. The core competencies in hospital medicine: a framework for curriculum development. J Hosp Med. 2006;1:167.
  63. Cherlin E, Morris V, Morris J, Johnson‐Hurzeler R, Sullivan GM, Bradley EH. Common myths about caring for patients with terminal illness: opportunities to improve care in the hospital setting. J Hosp Med. 2007;2:357365.
  64. Auerbach A. End‐of‐life care in a voluntary hospitalist model: effects on communication, processes of care, and patient symptoms. Am J Med. 2004;116:669675.
  65. Lindenauer PK, Pantilat SZ, Katz PP, Watcher RM. Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians. Ann Intern Med. 1999;130:343349.
  66. Muir JC, Arnold RM. Palliative care and hospitalist: an opportunity for cross‐fertilization. Am J Med. 2001;111(suppl):10S14S.
  67. Hertz B. Giving hospitalists their space. ACP Hospitalist. February 2008.
  68. Gesensway D. Having problems findings your patients? Today's Hospitalists. June 2010.
  69. O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 24(11):12231227.
  70. Medical Group Management Association and Society of Hospital Medicine (SHM). State of Hospital Medicine 2010 Report Based on 2009. Available online at http://www.mgma.com/store/Surveys‐and‐Benchmarking/State‐of‐Hospital‐Medicine‐2010‐Report‐Based‐on‐2009 ‐Data‐Print‐Edition/.
  71. Aiken LH, Clarke SP, Sloane DM, et al. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002;288(16):19871993.
  72. Bucheli B, Martina B. Reduced length of stay in medical emergency department patients: a prospective controlled study on emergency physician staffing. Eur J Emerg Med. 2004;11(1):2934.
  73. Hinami K, Whelan CT, Konetzka RT, Meltzer DO. Provider expectations and experiences of comanagement. J Hosp Med. 2011;6(7):401404.
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  36. Abenhaim HA, Kahn SR, Raffoul J, Becker MR. Program description: a hospitalist‐run medical short‐stay unit in a teaching hospital. Can Med Assoc J. 2000:163(11):14771480.
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  39. McCaig LF, Nawar EW. National Hospital Ambulatory Medical Care survey: 2004 emergency department summary. Adv Data. 2006;23:129.
  40. Wilkinson K, Severance H. Identification of chest pain patients appropriate for an emergency department observation unit. Emerg Med Clin North Am. 2001;19:3566.
  41. Chandra A, Rudraiah L, Zalenski RJ. Stress testing for risk stratification of patients with low to moderate probability of acute cardiac ischemia. Emerg Med Clin North Am. 2001;19:87103.
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  45. Gibler WB, Runyon JP, Levy RC, et al. A rapid diagnostic and treatment center for patients with chest pain in the emergency department. Ann Emerg Med. 1995;25:18.
  46. Gomez MA, Anderson JL, Karagounis LA, Muhlestein JB, Mooers FB. An emergency department‐based protocol for rapidly ruling out myocardial ischemia reduces hospital time and expense: results of a randomized study (ROMIO). J Am Coll Cardiol. 1996;28:2533.
  47. Goodacre S, Nicholl J, Dixon S, et al. Randomized controlled trial and economic evaluation of a chest pain observation unit compared with routine care. BMJ. 2004;328:254.
  48. Krantz MJ, Zwang 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(2):5558.
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  52. Darves B. Taking charge of observation units. Today's Hospitalist. July 2007.
  53. Fatovich DM, Nagree Y, Spirvulis P. Access block cause emergency department overcrowding and ambulance diversion in Perth, Western Australia. Emerg Med J. 2005;22:351354.
  54. Nicholl J, West J, Goodacre S, Tuner J. The relationship between distance to hospital and patient mortality in emergencies: an observational study. Emerg Med J. 2007;24:665668.
  55. Hoot N, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52:126136.
  56. Howell E, Bessman E, Kravat S, Kolodner K, Marshall R, Wright S. Active bed management by hospitalists and emergency department throughput. Ann Intern Med. 2008;149:804810.
  57. Chadaga S, Mancini D, Mehler PS, et al. A hospitalist‐led emergency department team improves hospital bed efficiency. J Hosp Med. 2010;5(suppl 1):1718.
  58. Society of Hospital Medicine. Perioperative care (a special supplement to The Hospitalist). Philadelphia, PA: Society of Hospital Medicine; 2005. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Home136:591596.
  59. Hospitalist Management Advisor. Hospitalist branch into preoperative medicine with preop assessments. Marblehead, MA: HCPro, 2006. Available at: http://www.hcpro.com/HOM‐57460–3615/Hospitalists‐branch‐into‐perioperative‐medicine‐with‐preop‐assessments.html. Accessed February 15, 2012.
  60. Magallanes M. The preoperative medicine service: an innovative practice at Kaiser Bellflower Medical Center. The Permanente Journal. 2002;6:1316.
  61. Darves B. A preop evaluation service delivers unexpected benefits. Today's Hospitalist. January 2008.
  62. Pistoria MJ, Amin AN, Dressler DD, McKean SCW, Budnitz TL. The core competencies in hospital medicine: a framework for curriculum development. J Hosp Med. 2006;1:167.
  63. Cherlin E, Morris V, Morris J, Johnson‐Hurzeler R, Sullivan GM, Bradley EH. Common myths about caring for patients with terminal illness: opportunities to improve care in the hospital setting. J Hosp Med. 2007;2:357365.
  64. Auerbach A. End‐of‐life care in a voluntary hospitalist model: effects on communication, processes of care, and patient symptoms. Am J Med. 2004;116:669675.
  65. Lindenauer PK, Pantilat SZ, Katz PP, Watcher RM. Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians. Ann Intern Med. 1999;130:343349.
  66. Muir JC, Arnold RM. Palliative care and hospitalist: an opportunity for cross‐fertilization. Am J Med. 2001;111(suppl):10S14S.
  67. Hertz B. Giving hospitalists their space. ACP Hospitalist. February 2008.
  68. Gesensway D. Having problems findings your patients? Today's Hospitalists. June 2010.
  69. O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 24(11):12231227.
  70. Medical Group Management Association and Society of Hospital Medicine (SHM). State of Hospital Medicine 2010 Report Based on 2009. Available online at http://www.mgma.com/store/Surveys‐and‐Benchmarking/State‐of‐Hospital‐Medicine‐2010‐Report‐Based‐on‐2009 ‐Data‐Print‐Edition/.
  71. Aiken LH, Clarke SP, Sloane DM, et al. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002;288(16):19871993.
  72. Bucheli B, Martina B. Reduced length of stay in medical emergency department patients: a prospective controlled study on emergency physician staffing. Eur J Emerg Med. 2004;11(1):2934.
  73. Hinami K, Whelan CT, Konetzka RT, Meltzer DO. Provider expectations and experiences of comanagement. J Hosp Med. 2011;6(7):401404.
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Hospitalist Physical Diagnosis Curricula

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Development of a hospitalist‐led‐and‐directed physical examination curriculum

Deficiencies in physical examination skills among medical students, housestaff, and even faculty have been reported for decades. For example, cardiac examination skills have been shown to improve during the early years of medical school and then plateau by the third year, with no measurable improvement through residency or beyond without fellowship training.13

Despite efforts by residency programs to promote bedside teaching, many general medicine faculty shy away due to lack of confidence and comfort in teaching physical diagnosis.4 The presence of hospitalists increases satisfaction among trainees with inpatient teaching,5 and medical faculty's ability has been shown to positively impact medical students' test scores.6 This lack of bedside teaching is a missed opportunity, as general medicine faculty are assuming more teaching responsibility.

The Merrin Bedside Teaching Program was founded in 2004 at New York University (NYU) School of Medicine to improve the quality of bedside teaching in the Department of Medicine/Division of General Internal Medicine. In this article, we describe the development process and early outcomes of this program.

METHODS

The principal teaching institution of NYU is Bellevue Hospital, an 800‐bed tertiary care safety‐net facility located in New York City. Approximately 30 general medicine faculty members at Bellevue Hospital attend on the inpatient teaching service 12 weeks per year each, with the remaining time spent in the hospital‐based clinics and other inpatient services.

Prior to the initiation of this program, our faculty did not receive formal instruction in bedside teaching, in general, or in teaching physical diagnosis, in particular. Housestaff and students were reporting that bedside rounds were not being conducted consistently and were of variable educational quality. Among a number of our residency and fellowship programs, there were concerns about meeting Accreditation Counsel for Graduate Medical Education (ACGME) requirements for quantity and quality of bedside rounds and faculty development.

To inform program development, we conducted a literature review, a series of focus groups with residents and faculty, and a survey of 39 general medicine hospital and ambulatory‐based clinician teachers to determine the perceived bedside teaching faculty development needs of our department. Then, over the next 2 years, we recruited 32 hospitalists and fellowship faculty to participate in a videotaped bedside teaching simulation. Each attending reviewed the videotape one‐on‐one with an experienced facilitator (A.K.), who elicited the attending's goals and instructional models and methods used for clinical teaching. To address the wide variety in teaching approaches identified in the assessment, 4 to 6 attendings met weekly for 1 month to observe each other conducting rounds with their teams. The facilitator of these groups used materials derived from relevant medical education theory79 and related conceptual models to frame the debriefing. Participants enthusiastically supported the educational value of this learner‐centered, experiential teaching approach.

We integrated this needs assessment with an individualized approach, incorporating learner goal‐setting with interactive and highly experiential teaching strategies,7,9,10 to create the Merrin Bedside Teaching Program in 2004. This program recruits faculty, with reputations as excellent teachers, to design a program to develop their own bedside teaching skills and disseminate what they learn to their peers. Faculty fellows are recruited through an open call for applications, which includes a letter of support from a supervisor and a detailed independent learning plan, including an identified mentor. Fellows are selected by the program's executive committee based on the likelihood of the success of their proposed program. A stipend equivalent of between 5% and 10% of base salary is provided to each fellow for a period of 2 years. Selected faculty fellows are encouraged to focus on an aspect of the physical examination, work in groups of 2 or 3, and to identify and recruit a mentor who is considered a master clinician in the target specialty. Master clinicians are given an honorarium to acknowledge their selection and incentivize them to spend time with the faculty fellows. This is funded with philanthropic support from the Merrin Family Foundation.

Fellows are guided by program leadership in their independent study, development of clinical teaching skills, and curriculum development using the same theory‐driven, systematic approach that framed program inception.911 Bedside rounds are the core instructional method used by each group of fellows and are supplemented by lectures, interactive small‐group seminars, and Web‐based modules in certain cases. Bedside sessions are run by the master‐clinician mentor until the faculty fellows are deemed competent by the mentor and feel confident enough to lead independently.

Since 2004, there have been 14 fellows who have developed programs focused on the examination of the heart, skin, knee, and shoulder. Program development is underway in motivational interviewing, the pulmonary examination, and the examination of the critically ill patient. We describe the work of the first 4 fellows as an example of how this fellowship creates value for the individual fellows, our departmental teaching programs, and the medical school.

Our first cohort of fellows chose, out of personal interest, to concentrate on the cardiac examination. They spent the first year working with highly respected cardiologists to hone their own clinical skills, reviewing the literature on the evidence‐based cardiac physical examination and effective teaching methods,12,13 researching the use of electronic stethoscopes and related technology for teaching at the bedside, and piloting a variety of approaches to teaching their busy colleagues these skills.

Bedside rounds focus on pertinent physical findings with an emphasis on an evidence‐based approach. We find we are most effective when the patient's diagnosis is unknown by the group leader to avoid bias when formulating the differential diagnosis. Sessions include a discussion of how the exam correlates with the diagnosis, relevant pathophysiology, imaging, and treatment options.

Two, 1‐hour‐long lectures in cardiac examination are delivered: the first reviews basics of heart sounds, both normal and abnormal, and the second reviews the most common systolic and diastolic murmurs. These lectures, scheduled into routine faculty conference time, utilize a PowerPoint format, with an overview of basic physiology and pathophysiology, aided by phonocardiograms, frequency spectrographs, and audio recordings delivered via a loudspeaker. Interactive cases offered by Blaufuss Multimedia (Rolling Hills Estates, CA) are an excellent teaching tool that incorporate case presentations, videos of key physical findings, auscultatory recordings, and relevant pathophysiology. We initially used this resource because of its high quality and ease of use; we now use our own interactive case presentations, which allow for flexibility with content and style, and which reinforce the prevalence of interesting cases at our institution to the audience members.

Technology has proven to be an invaluable tool in teaching cardiac physical diagnosis, both at the bedside and in the classroom. Electronic stethoscopes provide the ability to record heart sounds for use in teaching venues on short notice, such as morning report, and for use in creating the interactive case presentations described above. The electronic stethoscopes we use can be wired to peripheral devices, such as camcorders, iPods, and speaker pads. Speaker pads are devices, approximately the size of a stethoscope head, that can be connected by wires in series, each attached to a stethoscope, allowing a group of people to listen to the same sounds simultaneously with excellent sound reproduction. This technology allows each person standing around the bedside to listen to a patient while the group leader auscultates and explains the findings in real time. There are distinct advantages of simultaneous auscultation both for describing auditory findings and minimizing discomfort to the patient.

Applications are available for the iPod (Stethoscope App, Thinklabs Technology, LLC, Centennial, CO) which can record and display real‐time phonocardiography when attached to an electronic stethoscope, even at the terminus of a speaker pad chain. This application also allows recorded sounds to be played directly through a speaker, or transferred to a computer with the corresponding phonocardiographic and spectrographic images, that can all be incorporated into an interactive case presentation. Frequency spectrographs allow visualization of differences between low‐ and high‐frequency sounds, which, in conjunction with the timing and amplitude displayed by phonocardiography, can aid in teaching subtle findings, such as shapes of murmurs, patterns of splitting, gallups, etc.14 Playback of heart sounds in a conference room setting can be challenging, given the often subtle and low‐frequency findings typical of cardiac pathology, and is effectively achieved by using a musician‐quality loudspeaker. We have found that speaker pads offer the best sound quality at the bedside, although they are inconvenient for larger groups.

DISCUSSION

A new framework has been proposed for considering faculty development programs that focuses on the participants, program, content, facilitator, and the context in which the program occurs.15 We have effectively addressed and synthesized these components in a rich, high impact, learner‐centered faculty development program that also responds to challenges raised by changes in the health delivery system, concerns about accreditation requirements, and targeted local needs assessment.

We have been fortunate to recruit specialty faculty who are outstanding teachers, have welcomed the fellows into their clinics, and have dedicated countless hours to supervision and education. An unintended, but important, outcome of the program is that we are able to highlight the exceptional skills of our senior, experienced clinicians. These are colleagues who all too often do not receive adequate recognition in the modern‐day academic medical center environment, but who are undoubtedly invaluable to the education mission of these centers.

The existence of the program has resulted in our general medicine faculty showing great enthusiasm, both to develop an area of expertise and to participate as learners in the programs developed by peers. The faculty fellows in each specialty have become a valuable resource to peer faculty, residents, and medical students alike, who are now less dependent on consultants to identify and explain physical findings. The faculty teaching the knee and shoulder exams started a Sports Medicine Clinic within primary care, and assist with joint injections throughout the clinic. In addition to providing clinical support, their educational curriculum is included in both the attending and housestaff conference schedules. The cardiac lectures, both didactic and interactive case presentations, are included in the attending conference schedule, intern and resident core curricula, and the third‐year medicine clerkship lecture series. The dermatology group has created a series of comprehensive online modules that provide content tailored to general medicine. All this durable material is available broadly to trainees of all professions in our medical center.

Given the ever‐growing burden of patient care and extra‐clinical responsibilities, the principal factor limiting the effectiveness of bedside rounds is faculty availability. Despite this, all of our hospitalists have attended at least 1 bedside cardiac session, and the majority have attended multiple times. Varying the time and day of the sessions, offering to join attending rounds, and being available for impromptu diagnostic consultations have maximized the fellows' contact with faculty, residents, and students.

Although funding for evaluation of the program has been limited, a research agenda is emerging. Both the pulmonary physical exam and critical care groups are in the process of evaluating the effectiveness of their programs on the quality of bedside rounds, student and resident learning, and, to the extent possible, on patient care.

CONCLUSION

We believe wholeheartedly that bedside instruction both in physical diagnosis and interview skills must not become a lost art. General medicine faculty are ideally situated to take on this challenge. An educational program targeting hospitalists and general medicine faculty energizes faculty and leverages local resources to fill in gaps in skills for faculty and then for trainees. Generalist faculty relish the opportunity to champion a particular element of the doctorpatient encounter, which has contributed to our ultimate goal of strengthening the core diagnostic skills of our faculty who are at the forefront of clinical care and medical education.

Acknowledgements

The authors thank the Merrin Family for their generous support of the program; Drs Gregory Mints, Tanping Wong, and Sabrina Felson for their initial work in developing the Merrin Faculty Development Program; and Dr Martin Kahn for his tireless dedication to mentorship and bedside teaching.

Disclosure: Drs Janjigian, Charap, and Kalet report receiving funding from the Merrin Family Foundation.

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  13. Vukanovic‐Criley JM, Boker JR, Criley SR, Rajagopalan S, Criley JM. Using virtual patients to improve cardiac examination competency in medical students. Clin Cardiol. 2008;31(7):334339.
  14. Tavel ME. Cardiac auscultation: a glorious past—and it does have a future! Circulation. 2006;113(9):12551259.
  15. O'Sullivan PS, Irby DM. Reframing research on faculty development. Acad Med. 2011;86(4):421428.
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Deficiencies in physical examination skills among medical students, housestaff, and even faculty have been reported for decades. For example, cardiac examination skills have been shown to improve during the early years of medical school and then plateau by the third year, with no measurable improvement through residency or beyond without fellowship training.13

Despite efforts by residency programs to promote bedside teaching, many general medicine faculty shy away due to lack of confidence and comfort in teaching physical diagnosis.4 The presence of hospitalists increases satisfaction among trainees with inpatient teaching,5 and medical faculty's ability has been shown to positively impact medical students' test scores.6 This lack of bedside teaching is a missed opportunity, as general medicine faculty are assuming more teaching responsibility.

The Merrin Bedside Teaching Program was founded in 2004 at New York University (NYU) School of Medicine to improve the quality of bedside teaching in the Department of Medicine/Division of General Internal Medicine. In this article, we describe the development process and early outcomes of this program.

METHODS

The principal teaching institution of NYU is Bellevue Hospital, an 800‐bed tertiary care safety‐net facility located in New York City. Approximately 30 general medicine faculty members at Bellevue Hospital attend on the inpatient teaching service 12 weeks per year each, with the remaining time spent in the hospital‐based clinics and other inpatient services.

Prior to the initiation of this program, our faculty did not receive formal instruction in bedside teaching, in general, or in teaching physical diagnosis, in particular. Housestaff and students were reporting that bedside rounds were not being conducted consistently and were of variable educational quality. Among a number of our residency and fellowship programs, there were concerns about meeting Accreditation Counsel for Graduate Medical Education (ACGME) requirements for quantity and quality of bedside rounds and faculty development.

To inform program development, we conducted a literature review, a series of focus groups with residents and faculty, and a survey of 39 general medicine hospital and ambulatory‐based clinician teachers to determine the perceived bedside teaching faculty development needs of our department. Then, over the next 2 years, we recruited 32 hospitalists and fellowship faculty to participate in a videotaped bedside teaching simulation. Each attending reviewed the videotape one‐on‐one with an experienced facilitator (A.K.), who elicited the attending's goals and instructional models and methods used for clinical teaching. To address the wide variety in teaching approaches identified in the assessment, 4 to 6 attendings met weekly for 1 month to observe each other conducting rounds with their teams. The facilitator of these groups used materials derived from relevant medical education theory79 and related conceptual models to frame the debriefing. Participants enthusiastically supported the educational value of this learner‐centered, experiential teaching approach.

We integrated this needs assessment with an individualized approach, incorporating learner goal‐setting with interactive and highly experiential teaching strategies,7,9,10 to create the Merrin Bedside Teaching Program in 2004. This program recruits faculty, with reputations as excellent teachers, to design a program to develop their own bedside teaching skills and disseminate what they learn to their peers. Faculty fellows are recruited through an open call for applications, which includes a letter of support from a supervisor and a detailed independent learning plan, including an identified mentor. Fellows are selected by the program's executive committee based on the likelihood of the success of their proposed program. A stipend equivalent of between 5% and 10% of base salary is provided to each fellow for a period of 2 years. Selected faculty fellows are encouraged to focus on an aspect of the physical examination, work in groups of 2 or 3, and to identify and recruit a mentor who is considered a master clinician in the target specialty. Master clinicians are given an honorarium to acknowledge their selection and incentivize them to spend time with the faculty fellows. This is funded with philanthropic support from the Merrin Family Foundation.

Fellows are guided by program leadership in their independent study, development of clinical teaching skills, and curriculum development using the same theory‐driven, systematic approach that framed program inception.911 Bedside rounds are the core instructional method used by each group of fellows and are supplemented by lectures, interactive small‐group seminars, and Web‐based modules in certain cases. Bedside sessions are run by the master‐clinician mentor until the faculty fellows are deemed competent by the mentor and feel confident enough to lead independently.

Since 2004, there have been 14 fellows who have developed programs focused on the examination of the heart, skin, knee, and shoulder. Program development is underway in motivational interviewing, the pulmonary examination, and the examination of the critically ill patient. We describe the work of the first 4 fellows as an example of how this fellowship creates value for the individual fellows, our departmental teaching programs, and the medical school.

Our first cohort of fellows chose, out of personal interest, to concentrate on the cardiac examination. They spent the first year working with highly respected cardiologists to hone their own clinical skills, reviewing the literature on the evidence‐based cardiac physical examination and effective teaching methods,12,13 researching the use of electronic stethoscopes and related technology for teaching at the bedside, and piloting a variety of approaches to teaching their busy colleagues these skills.

Bedside rounds focus on pertinent physical findings with an emphasis on an evidence‐based approach. We find we are most effective when the patient's diagnosis is unknown by the group leader to avoid bias when formulating the differential diagnosis. Sessions include a discussion of how the exam correlates with the diagnosis, relevant pathophysiology, imaging, and treatment options.

Two, 1‐hour‐long lectures in cardiac examination are delivered: the first reviews basics of heart sounds, both normal and abnormal, and the second reviews the most common systolic and diastolic murmurs. These lectures, scheduled into routine faculty conference time, utilize a PowerPoint format, with an overview of basic physiology and pathophysiology, aided by phonocardiograms, frequency spectrographs, and audio recordings delivered via a loudspeaker. Interactive cases offered by Blaufuss Multimedia (Rolling Hills Estates, CA) are an excellent teaching tool that incorporate case presentations, videos of key physical findings, auscultatory recordings, and relevant pathophysiology. We initially used this resource because of its high quality and ease of use; we now use our own interactive case presentations, which allow for flexibility with content and style, and which reinforce the prevalence of interesting cases at our institution to the audience members.

Technology has proven to be an invaluable tool in teaching cardiac physical diagnosis, both at the bedside and in the classroom. Electronic stethoscopes provide the ability to record heart sounds for use in teaching venues on short notice, such as morning report, and for use in creating the interactive case presentations described above. The electronic stethoscopes we use can be wired to peripheral devices, such as camcorders, iPods, and speaker pads. Speaker pads are devices, approximately the size of a stethoscope head, that can be connected by wires in series, each attached to a stethoscope, allowing a group of people to listen to the same sounds simultaneously with excellent sound reproduction. This technology allows each person standing around the bedside to listen to a patient while the group leader auscultates and explains the findings in real time. There are distinct advantages of simultaneous auscultation both for describing auditory findings and minimizing discomfort to the patient.

Applications are available for the iPod (Stethoscope App, Thinklabs Technology, LLC, Centennial, CO) which can record and display real‐time phonocardiography when attached to an electronic stethoscope, even at the terminus of a speaker pad chain. This application also allows recorded sounds to be played directly through a speaker, or transferred to a computer with the corresponding phonocardiographic and spectrographic images, that can all be incorporated into an interactive case presentation. Frequency spectrographs allow visualization of differences between low‐ and high‐frequency sounds, which, in conjunction with the timing and amplitude displayed by phonocardiography, can aid in teaching subtle findings, such as shapes of murmurs, patterns of splitting, gallups, etc.14 Playback of heart sounds in a conference room setting can be challenging, given the often subtle and low‐frequency findings typical of cardiac pathology, and is effectively achieved by using a musician‐quality loudspeaker. We have found that speaker pads offer the best sound quality at the bedside, although they are inconvenient for larger groups.

DISCUSSION

A new framework has been proposed for considering faculty development programs that focuses on the participants, program, content, facilitator, and the context in which the program occurs.15 We have effectively addressed and synthesized these components in a rich, high impact, learner‐centered faculty development program that also responds to challenges raised by changes in the health delivery system, concerns about accreditation requirements, and targeted local needs assessment.

We have been fortunate to recruit specialty faculty who are outstanding teachers, have welcomed the fellows into their clinics, and have dedicated countless hours to supervision and education. An unintended, but important, outcome of the program is that we are able to highlight the exceptional skills of our senior, experienced clinicians. These are colleagues who all too often do not receive adequate recognition in the modern‐day academic medical center environment, but who are undoubtedly invaluable to the education mission of these centers.

The existence of the program has resulted in our general medicine faculty showing great enthusiasm, both to develop an area of expertise and to participate as learners in the programs developed by peers. The faculty fellows in each specialty have become a valuable resource to peer faculty, residents, and medical students alike, who are now less dependent on consultants to identify and explain physical findings. The faculty teaching the knee and shoulder exams started a Sports Medicine Clinic within primary care, and assist with joint injections throughout the clinic. In addition to providing clinical support, their educational curriculum is included in both the attending and housestaff conference schedules. The cardiac lectures, both didactic and interactive case presentations, are included in the attending conference schedule, intern and resident core curricula, and the third‐year medicine clerkship lecture series. The dermatology group has created a series of comprehensive online modules that provide content tailored to general medicine. All this durable material is available broadly to trainees of all professions in our medical center.

Given the ever‐growing burden of patient care and extra‐clinical responsibilities, the principal factor limiting the effectiveness of bedside rounds is faculty availability. Despite this, all of our hospitalists have attended at least 1 bedside cardiac session, and the majority have attended multiple times. Varying the time and day of the sessions, offering to join attending rounds, and being available for impromptu diagnostic consultations have maximized the fellows' contact with faculty, residents, and students.

Although funding for evaluation of the program has been limited, a research agenda is emerging. Both the pulmonary physical exam and critical care groups are in the process of evaluating the effectiveness of their programs on the quality of bedside rounds, student and resident learning, and, to the extent possible, on patient care.

CONCLUSION

We believe wholeheartedly that bedside instruction both in physical diagnosis and interview skills must not become a lost art. General medicine faculty are ideally situated to take on this challenge. An educational program targeting hospitalists and general medicine faculty energizes faculty and leverages local resources to fill in gaps in skills for faculty and then for trainees. Generalist faculty relish the opportunity to champion a particular element of the doctorpatient encounter, which has contributed to our ultimate goal of strengthening the core diagnostic skills of our faculty who are at the forefront of clinical care and medical education.

Acknowledgements

The authors thank the Merrin Family for their generous support of the program; Drs Gregory Mints, Tanping Wong, and Sabrina Felson for their initial work in developing the Merrin Faculty Development Program; and Dr Martin Kahn for his tireless dedication to mentorship and bedside teaching.

Disclosure: Drs Janjigian, Charap, and Kalet report receiving funding from the Merrin Family Foundation.

Deficiencies in physical examination skills among medical students, housestaff, and even faculty have been reported for decades. For example, cardiac examination skills have been shown to improve during the early years of medical school and then plateau by the third year, with no measurable improvement through residency or beyond without fellowship training.13

Despite efforts by residency programs to promote bedside teaching, many general medicine faculty shy away due to lack of confidence and comfort in teaching physical diagnosis.4 The presence of hospitalists increases satisfaction among trainees with inpatient teaching,5 and medical faculty's ability has been shown to positively impact medical students' test scores.6 This lack of bedside teaching is a missed opportunity, as general medicine faculty are assuming more teaching responsibility.

The Merrin Bedside Teaching Program was founded in 2004 at New York University (NYU) School of Medicine to improve the quality of bedside teaching in the Department of Medicine/Division of General Internal Medicine. In this article, we describe the development process and early outcomes of this program.

METHODS

The principal teaching institution of NYU is Bellevue Hospital, an 800‐bed tertiary care safety‐net facility located in New York City. Approximately 30 general medicine faculty members at Bellevue Hospital attend on the inpatient teaching service 12 weeks per year each, with the remaining time spent in the hospital‐based clinics and other inpatient services.

Prior to the initiation of this program, our faculty did not receive formal instruction in bedside teaching, in general, or in teaching physical diagnosis, in particular. Housestaff and students were reporting that bedside rounds were not being conducted consistently and were of variable educational quality. Among a number of our residency and fellowship programs, there were concerns about meeting Accreditation Counsel for Graduate Medical Education (ACGME) requirements for quantity and quality of bedside rounds and faculty development.

To inform program development, we conducted a literature review, a series of focus groups with residents and faculty, and a survey of 39 general medicine hospital and ambulatory‐based clinician teachers to determine the perceived bedside teaching faculty development needs of our department. Then, over the next 2 years, we recruited 32 hospitalists and fellowship faculty to participate in a videotaped bedside teaching simulation. Each attending reviewed the videotape one‐on‐one with an experienced facilitator (A.K.), who elicited the attending's goals and instructional models and methods used for clinical teaching. To address the wide variety in teaching approaches identified in the assessment, 4 to 6 attendings met weekly for 1 month to observe each other conducting rounds with their teams. The facilitator of these groups used materials derived from relevant medical education theory79 and related conceptual models to frame the debriefing. Participants enthusiastically supported the educational value of this learner‐centered, experiential teaching approach.

We integrated this needs assessment with an individualized approach, incorporating learner goal‐setting with interactive and highly experiential teaching strategies,7,9,10 to create the Merrin Bedside Teaching Program in 2004. This program recruits faculty, with reputations as excellent teachers, to design a program to develop their own bedside teaching skills and disseminate what they learn to their peers. Faculty fellows are recruited through an open call for applications, which includes a letter of support from a supervisor and a detailed independent learning plan, including an identified mentor. Fellows are selected by the program's executive committee based on the likelihood of the success of their proposed program. A stipend equivalent of between 5% and 10% of base salary is provided to each fellow for a period of 2 years. Selected faculty fellows are encouraged to focus on an aspect of the physical examination, work in groups of 2 or 3, and to identify and recruit a mentor who is considered a master clinician in the target specialty. Master clinicians are given an honorarium to acknowledge their selection and incentivize them to spend time with the faculty fellows. This is funded with philanthropic support from the Merrin Family Foundation.

Fellows are guided by program leadership in their independent study, development of clinical teaching skills, and curriculum development using the same theory‐driven, systematic approach that framed program inception.911 Bedside rounds are the core instructional method used by each group of fellows and are supplemented by lectures, interactive small‐group seminars, and Web‐based modules in certain cases. Bedside sessions are run by the master‐clinician mentor until the faculty fellows are deemed competent by the mentor and feel confident enough to lead independently.

Since 2004, there have been 14 fellows who have developed programs focused on the examination of the heart, skin, knee, and shoulder. Program development is underway in motivational interviewing, the pulmonary examination, and the examination of the critically ill patient. We describe the work of the first 4 fellows as an example of how this fellowship creates value for the individual fellows, our departmental teaching programs, and the medical school.

Our first cohort of fellows chose, out of personal interest, to concentrate on the cardiac examination. They spent the first year working with highly respected cardiologists to hone their own clinical skills, reviewing the literature on the evidence‐based cardiac physical examination and effective teaching methods,12,13 researching the use of electronic stethoscopes and related technology for teaching at the bedside, and piloting a variety of approaches to teaching their busy colleagues these skills.

Bedside rounds focus on pertinent physical findings with an emphasis on an evidence‐based approach. We find we are most effective when the patient's diagnosis is unknown by the group leader to avoid bias when formulating the differential diagnosis. Sessions include a discussion of how the exam correlates with the diagnosis, relevant pathophysiology, imaging, and treatment options.

Two, 1‐hour‐long lectures in cardiac examination are delivered: the first reviews basics of heart sounds, both normal and abnormal, and the second reviews the most common systolic and diastolic murmurs. These lectures, scheduled into routine faculty conference time, utilize a PowerPoint format, with an overview of basic physiology and pathophysiology, aided by phonocardiograms, frequency spectrographs, and audio recordings delivered via a loudspeaker. Interactive cases offered by Blaufuss Multimedia (Rolling Hills Estates, CA) are an excellent teaching tool that incorporate case presentations, videos of key physical findings, auscultatory recordings, and relevant pathophysiology. We initially used this resource because of its high quality and ease of use; we now use our own interactive case presentations, which allow for flexibility with content and style, and which reinforce the prevalence of interesting cases at our institution to the audience members.

Technology has proven to be an invaluable tool in teaching cardiac physical diagnosis, both at the bedside and in the classroom. Electronic stethoscopes provide the ability to record heart sounds for use in teaching venues on short notice, such as morning report, and for use in creating the interactive case presentations described above. The electronic stethoscopes we use can be wired to peripheral devices, such as camcorders, iPods, and speaker pads. Speaker pads are devices, approximately the size of a stethoscope head, that can be connected by wires in series, each attached to a stethoscope, allowing a group of people to listen to the same sounds simultaneously with excellent sound reproduction. This technology allows each person standing around the bedside to listen to a patient while the group leader auscultates and explains the findings in real time. There are distinct advantages of simultaneous auscultation both for describing auditory findings and minimizing discomfort to the patient.

Applications are available for the iPod (Stethoscope App, Thinklabs Technology, LLC, Centennial, CO) which can record and display real‐time phonocardiography when attached to an electronic stethoscope, even at the terminus of a speaker pad chain. This application also allows recorded sounds to be played directly through a speaker, or transferred to a computer with the corresponding phonocardiographic and spectrographic images, that can all be incorporated into an interactive case presentation. Frequency spectrographs allow visualization of differences between low‐ and high‐frequency sounds, which, in conjunction with the timing and amplitude displayed by phonocardiography, can aid in teaching subtle findings, such as shapes of murmurs, patterns of splitting, gallups, etc.14 Playback of heart sounds in a conference room setting can be challenging, given the often subtle and low‐frequency findings typical of cardiac pathology, and is effectively achieved by using a musician‐quality loudspeaker. We have found that speaker pads offer the best sound quality at the bedside, although they are inconvenient for larger groups.

DISCUSSION

A new framework has been proposed for considering faculty development programs that focuses on the participants, program, content, facilitator, and the context in which the program occurs.15 We have effectively addressed and synthesized these components in a rich, high impact, learner‐centered faculty development program that also responds to challenges raised by changes in the health delivery system, concerns about accreditation requirements, and targeted local needs assessment.

We have been fortunate to recruit specialty faculty who are outstanding teachers, have welcomed the fellows into their clinics, and have dedicated countless hours to supervision and education. An unintended, but important, outcome of the program is that we are able to highlight the exceptional skills of our senior, experienced clinicians. These are colleagues who all too often do not receive adequate recognition in the modern‐day academic medical center environment, but who are undoubtedly invaluable to the education mission of these centers.

The existence of the program has resulted in our general medicine faculty showing great enthusiasm, both to develop an area of expertise and to participate as learners in the programs developed by peers. The faculty fellows in each specialty have become a valuable resource to peer faculty, residents, and medical students alike, who are now less dependent on consultants to identify and explain physical findings. The faculty teaching the knee and shoulder exams started a Sports Medicine Clinic within primary care, and assist with joint injections throughout the clinic. In addition to providing clinical support, their educational curriculum is included in both the attending and housestaff conference schedules. The cardiac lectures, both didactic and interactive case presentations, are included in the attending conference schedule, intern and resident core curricula, and the third‐year medicine clerkship lecture series. The dermatology group has created a series of comprehensive online modules that provide content tailored to general medicine. All this durable material is available broadly to trainees of all professions in our medical center.

Given the ever‐growing burden of patient care and extra‐clinical responsibilities, the principal factor limiting the effectiveness of bedside rounds is faculty availability. Despite this, all of our hospitalists have attended at least 1 bedside cardiac session, and the majority have attended multiple times. Varying the time and day of the sessions, offering to join attending rounds, and being available for impromptu diagnostic consultations have maximized the fellows' contact with faculty, residents, and students.

Although funding for evaluation of the program has been limited, a research agenda is emerging. Both the pulmonary physical exam and critical care groups are in the process of evaluating the effectiveness of their programs on the quality of bedside rounds, student and resident learning, and, to the extent possible, on patient care.

CONCLUSION

We believe wholeheartedly that bedside instruction both in physical diagnosis and interview skills must not become a lost art. General medicine faculty are ideally situated to take on this challenge. An educational program targeting hospitalists and general medicine faculty energizes faculty and leverages local resources to fill in gaps in skills for faculty and then for trainees. Generalist faculty relish the opportunity to champion a particular element of the doctorpatient encounter, which has contributed to our ultimate goal of strengthening the core diagnostic skills of our faculty who are at the forefront of clinical care and medical education.

Acknowledgements

The authors thank the Merrin Family for their generous support of the program; Drs Gregory Mints, Tanping Wong, and Sabrina Felson for their initial work in developing the Merrin Faculty Development Program; and Dr Martin Kahn for his tireless dedication to mentorship and bedside teaching.

Disclosure: Drs Janjigian, Charap, and Kalet report receiving funding from the Merrin Family Foundation.

References
  1. Mangione S, Nieman LZ. Cardiac auscultatory skills of internal medicine and family practice trainees. A comparison of diagnostic proficiency. JAMA. 1997;278(9):717722.
  2. Vukanovic‐Criley JM, Criley S, Warde CM, et al. Competency in cardiac examination skills in medical students, trainees, physicians, and faculty: a multicenter study. Arch Intern Med. 2006;166(6):610616.
  3. Vukanovic‐Criley JM, Hovanesyan A, Criley SR, et al. Confidential testing of cardiac examination competency in cardiology and noncardiology faculty and trainees: a multicenter study. Clin Cardiol. 2010;33(12):738745.
  4. Ramani S, Orlander JD, Strunin L, Barber TW. Whither bedside teaching? A focus‐group study of clinical teachers. Acad Med. 2003;78(4):384390.
  5. Natarajan P, Ranji SR, Auerbach AD, Hauer KE. Effect of hospitalist attending physicians on trainee educational experiences: a systematic review. J Hosp Med. 2009;4(8):490498.
  6. Stern DT, Williams BC, Gill A, Gruppen LD, Woolliscroft JO, Grum CM. Is there a relationship between attending physicians' and residents' teaching skills and students' examination scores? Acad Med. 2000;75(11):11441146.
  7. Neher JO, Gordon KC, Meyer B, Stevens N. A five‐step “microskills” model of clinical teaching. J Am Board Fam Pract. 1992;5(4):419424.
  8. Wright SM, Kern DE, Kolodner K, Howard DM, Brancati FL. Attributes of excellent attending‐physician role models. N Engl J Med. 1998;339(27):19861993.
  9. Janicik RW, Fletcher KE. Teaching at the bedside: a new model. Med Teach. 2003;25(2):127130.
  10. Hewson MG. A theory‐based faculty development program for clinician‐educators. Acad Med. 2000;75(5):498501.
  11. Kern D, Thomas P, Howard D, Bass E, ed. Curriculum Development for Medical Education: A Six‐Step Approach. Baltimore, MD: The Johns Hopkins University Press; 1998.
  12. Criley JM, Keiner J, Boker JR, Criley SR, Warde CM. Innovative web‐based multimedia curriculum improves cardiac examination competency of residents. J Hosp Med. 2008;3(2):124133.
  13. Vukanovic‐Criley JM, Boker JR, Criley SR, Rajagopalan S, Criley JM. Using virtual patients to improve cardiac examination competency in medical students. Clin Cardiol. 2008;31(7):334339.
  14. Tavel ME. Cardiac auscultation: a glorious past—and it does have a future! Circulation. 2006;113(9):12551259.
  15. O'Sullivan PS, Irby DM. Reframing research on faculty development. Acad Med. 2011;86(4):421428.
References
  1. Mangione S, Nieman LZ. Cardiac auscultatory skills of internal medicine and family practice trainees. A comparison of diagnostic proficiency. JAMA. 1997;278(9):717722.
  2. Vukanovic‐Criley JM, Criley S, Warde CM, et al. Competency in cardiac examination skills in medical students, trainees, physicians, and faculty: a multicenter study. Arch Intern Med. 2006;166(6):610616.
  3. Vukanovic‐Criley JM, Hovanesyan A, Criley SR, et al. Confidential testing of cardiac examination competency in cardiology and noncardiology faculty and trainees: a multicenter study. Clin Cardiol. 2010;33(12):738745.
  4. Ramani S, Orlander JD, Strunin L, Barber TW. Whither bedside teaching? A focus‐group study of clinical teachers. Acad Med. 2003;78(4):384390.
  5. Natarajan P, Ranji SR, Auerbach AD, Hauer KE. Effect of hospitalist attending physicians on trainee educational experiences: a systematic review. J Hosp Med. 2009;4(8):490498.
  6. Stern DT, Williams BC, Gill A, Gruppen LD, Woolliscroft JO, Grum CM. Is there a relationship between attending physicians' and residents' teaching skills and students' examination scores? Acad Med. 2000;75(11):11441146.
  7. Neher JO, Gordon KC, Meyer B, Stevens N. A five‐step “microskills” model of clinical teaching. J Am Board Fam Pract. 1992;5(4):419424.
  8. Wright SM, Kern DE, Kolodner K, Howard DM, Brancati FL. Attributes of excellent attending‐physician role models. N Engl J Med. 1998;339(27):19861993.
  9. Janicik RW, Fletcher KE. Teaching at the bedside: a new model. Med Teach. 2003;25(2):127130.
  10. Hewson MG. A theory‐based faculty development program for clinician‐educators. Acad Med. 2000;75(5):498501.
  11. Kern D, Thomas P, Howard D, Bass E, ed. Curriculum Development for Medical Education: A Six‐Step Approach. Baltimore, MD: The Johns Hopkins University Press; 1998.
  12. Criley JM, Keiner J, Boker JR, Criley SR, Warde CM. Innovative web‐based multimedia curriculum improves cardiac examination competency of residents. J Hosp Med. 2008;3(2):124133.
  13. Vukanovic‐Criley JM, Boker JR, Criley SR, Rajagopalan S, Criley JM. Using virtual patients to improve cardiac examination competency in medical students. Clin Cardiol. 2008;31(7):334339.
  14. Tavel ME. Cardiac auscultation: a glorious past—and it does have a future! Circulation. 2006;113(9):12551259.
  15. O'Sullivan PS, Irby DM. Reframing research on faculty development. Acad Med. 2011;86(4):421428.
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