In 2003, the Accreditation Council for Graduate Medical Education (ACGME) announced the first in a series of guidelines related to the regulation and oversight of residency training.1 The initial iteration specifically focused on the total and consecutive numbers of duty hours worked by trainees. These limitations began a new era of shift work in internal medicine residency training. With decreases in housestaff admitting capacity, clinical work has frequently been offloaded to non‐teaching or attending‐only services, increasing the demand for hospitalists to fill the void in physician‐staffed care in the hospital.2, 3 Since the implementation of the 2003 ACGME guidelines and a growing focus on patient safety, there has been increased study of, and call for, oversight of trainees in medicine; among these was the 2008 Institute of Medicine report,4 calling for 24/7 attending‐level supervision. The updated ACGME requirements,5 effective July 1, 2011, mandate enhanced on‐site supervision of trainee physicians. These new regulations not only define varying levels of supervision for trainees, including direct supervision with the physical presence of a supervisor and the degree of availability of said supervisor, they also describe ensuring the quality of supervision provided.5 While continuous attending‐level supervision is not yet mandated, many residency programs look to their academic hospitalists to fill the supervisory void, particularly at night. However, what specific roles hospitalists play in the nighttime supervision of trainees or the impact of this supervision remains unclear. To date, no study has examined a broad sample of hospitalist programs in teaching hospitals and the types of resident oversight they provide. We aimed to describe the current state of academic hospitalists in the clinical supervision of housestaff, specifically during the overnight period, and hospitalist perceptions of how the new ACGME requirements would impact traineehospitalist interactions.

METHODS

The Housestaff Oversight Subcommittee, a working group of the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force, surveyed a sample of academic hospitalist program leaders to assess the current status of trainee supervision performed by hospitalists. Programs were considered academic if they were located in the primary hospital of a residency that participates in the National Resident Matching Program for Internal Medicine. To obtain a broad geographic spectrum of academic hospitalist programs, all programs, both university and community‐based, in 4 states and 2 metropolitan regions were sampled: Washington, Oregon, Texas, Maryland, and the Philadelphia and Chicago metropolitan areas. Hospitalist program leaders were identified by members of the Taskforce using individual program websites and by querying departmental leadership at eligible teaching hospitals. Respondents were contacted by e‐mail for participation. None of the authors of the manuscript were participants in the survey.

The survey was developed by consensus of the working group after reviewing the salient literature and included additional questions queried to internal medicine program directors.6 The 19‐item SurveyMonkey instrument included questions about hospitalists' role in trainees' education and evaluation. A Likert‐type scale was used to assess perceptions regarding the impact of on‐site hospitalist supervision on trainee autonomy and hospitalist workload (1 = strongly disagree to 5 = strongly agree). Descriptive statistics were performed and, where appropriate, t test and Fisher's exact test were performed to identify associations between program characteristics and perceptions. Stata SE was used (STATA Corp, College Station, TX) for all statistical analysis.

RESULTS

The survey was sent to 47 individuals identified as likely hospitalist program leaders and completed by 41 individuals (87%). However, 7 respondents turned out not to be program leaders and were therefore excluded, resulting in a 72% (34/47) survey response rate.

The programs for which we did not obtain responses were similar to respondent programs, and did not include a larger proportion of community‐based programs or overrepresent a specific geographic region. Twenty‐five (73%) of the 34 hospitalist program leaders were male, with an average age of 44.3 years, and an average of 12 years post‐residency training (range, 530 years). They reported leading groups with an average of 18 full‐time equivalent (FTE) faculty (range, 350 persons).

DISCUSSION

This study provides important information about how academic hospitalists currently contribute to the supervision of internal medicine residents. While academic hospitalist groups frequently have faculty providing clinical care on‐site at night, and often hospitalists provide overnight supervision of internal medicine trainees, formal supervision of trainees is not uniform, and few hospitalists groups have a mechanism to provide training or faculty development on how to effectively supervise resident trainees. Hospitalist leaders expressed concerns that creating additional formal overnight supervisory responsibilities may add to an already burdened overnight hospitalist. Formalizing this supervisory role, including explicit role definitions and faculty training for trainee supervision, is necessary.

Though our sample size is small, we captured a diverse geographic range of both university and community‐based academic hospitalist programs by surveying group leaders in several distinct regions. We are unable to comment on differences between responding and non‐responding hospitalist programs, but there does not appear to be a systematic difference between these groups.

Our findings are consistent with work describing a lack of structured conceptual frameworks in effectively supervising trainees,7, 8 and also, at times, nebulous expectations for hospitalist faculty. We found that the existence of a formal supervisory policy within the associated residency program, as well as defined roles for hospitalists, increases the likelihood of positive perceptions of the new ACGME supervisory recommendations. However, the existence of these requirements does not mean that all programs are capable of following them. While additional discussion is required to best delineate a formal overnight hospitalist role in trainee supervision, clearly defining expectations for both faculty and trainees, and their interactions, may alleviate the struggles that exist in programs with ill‐defined roles for hospitalist faculty supervision. While faculty duty hours standards do not exist, additional duties of nighttime coverage for hospitalists suggests that close attention should be paid to burn‐out.9 Faculty development on nighttime supervision and teaching may help maximize both learning and patient care efficiency, and provide a framework for this often unstructured educational time.

In 2003, the Accreditation Council for Graduate Medical Education (ACGME) announced the first in a series of guidelines related to the regulation and oversight of residency training.1 The initial iteration specifically focused on the total and consecutive numbers of duty hours worked by trainees. These limitations began a new era of shift work in internal medicine residency training. With decreases in housestaff admitting capacity, clinical work has frequently been offloaded to non‐teaching or attending‐only services, increasing the demand for hospitalists to fill the void in physician‐staffed care in the hospital.2, 3 Since the implementation of the 2003 ACGME guidelines and a growing focus on patient safety, there has been increased study of, and call for, oversight of trainees in medicine; among these was the 2008 Institute of Medicine report,4 calling for 24/7 attending‐level supervision. The updated ACGME requirements,5 effective July 1, 2011, mandate enhanced on‐site supervision of trainee physicians. These new regulations not only define varying levels of supervision for trainees, including direct supervision with the physical presence of a supervisor and the degree of availability of said supervisor, they also describe ensuring the quality of supervision provided.5 While continuous attending‐level supervision is not yet mandated, many residency programs look to their academic hospitalists to fill the supervisory void, particularly at night. However, what specific roles hospitalists play in the nighttime supervision of trainees or the impact of this supervision remains unclear. To date, no study has examined a broad sample of hospitalist programs in teaching hospitals and the types of resident oversight they provide. We aimed to describe the current state of academic hospitalists in the clinical supervision of housestaff, specifically during the overnight period, and hospitalist perceptions of how the new ACGME requirements would impact traineehospitalist interactions.

METHODS

The Housestaff Oversight Subcommittee, a working group of the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force, surveyed a sample of academic hospitalist program leaders to assess the current status of trainee supervision performed by hospitalists. Programs were considered academic if they were located in the primary hospital of a residency that participates in the National Resident Matching Program for Internal Medicine. To obtain a broad geographic spectrum of academic hospitalist programs, all programs, both university and community‐based, in 4 states and 2 metropolitan regions were sampled: Washington, Oregon, Texas, Maryland, and the Philadelphia and Chicago metropolitan areas. Hospitalist program leaders were identified by members of the Taskforce using individual program websites and by querying departmental leadership at eligible teaching hospitals. Respondents were contacted by e‐mail for participation. None of the authors of the manuscript were participants in the survey.

The survey was developed by consensus of the working group after reviewing the salient literature and included additional questions queried to internal medicine program directors.6 The 19‐item SurveyMonkey instrument included questions about hospitalists' role in trainees' education and evaluation. A Likert‐type scale was used to assess perceptions regarding the impact of on‐site hospitalist supervision on trainee autonomy and hospitalist workload (1 = strongly disagree to 5 = strongly agree). Descriptive statistics were performed and, where appropriate, t test and Fisher's exact test were performed to identify associations between program characteristics and perceptions. Stata SE was used (STATA Corp, College Station, TX) for all statistical analysis.

RESULTS

The survey was sent to 47 individuals identified as likely hospitalist program leaders and completed by 41 individuals (87%). However, 7 respondents turned out not to be program leaders and were therefore excluded, resulting in a 72% (34/47) survey response rate.

The programs for which we did not obtain responses were similar to respondent programs, and did not include a larger proportion of community‐based programs or overrepresent a specific geographic region. Twenty‐five (73%) of the 34 hospitalist program leaders were male, with an average age of 44.3 years, and an average of 12 years post‐residency training (range, 530 years). They reported leading groups with an average of 18 full‐time equivalent (FTE) faculty (range, 350 persons).

DISCUSSION

This study provides important information about how academic hospitalists currently contribute to the supervision of internal medicine residents. While academic hospitalist groups frequently have faculty providing clinical care on‐site at night, and often hospitalists provide overnight supervision of internal medicine trainees, formal supervision of trainees is not uniform, and few hospitalists groups have a mechanism to provide training or faculty development on how to effectively supervise resident trainees. Hospitalist leaders expressed concerns that creating additional formal overnight supervisory responsibilities may add to an already burdened overnight hospitalist. Formalizing this supervisory role, including explicit role definitions and faculty training for trainee supervision, is necessary.

Though our sample size is small, we captured a diverse geographic range of both university and community‐based academic hospitalist programs by surveying group leaders in several distinct regions. We are unable to comment on differences between responding and non‐responding hospitalist programs, but there does not appear to be a systematic difference between these groups.

Our findings are consistent with work describing a lack of structured conceptual frameworks in effectively supervising trainees,7, 8 and also, at times, nebulous expectations for hospitalist faculty. We found that the existence of a formal supervisory policy within the associated residency program, as well as defined roles for hospitalists, increases the likelihood of positive perceptions of the new ACGME supervisory recommendations. However, the existence of these requirements does not mean that all programs are capable of following them. While additional discussion is required to best delineate a formal overnight hospitalist role in trainee supervision, clearly defining expectations for both faculty and trainees, and their interactions, may alleviate the struggles that exist in programs with ill‐defined roles for hospitalist faculty supervision. While faculty duty hours standards do not exist, additional duties of nighttime coverage for hospitalists suggests that close attention should be paid to burn‐out.9 Faculty development on nighttime supervision and teaching may help maximize both learning and patient care efficiency, and provide a framework for this often unstructured educational time.

In 2003, the Accreditation Council for Graduate Medical Education (ACGME) announced the first in a series of guidelines related to the regulation and oversight of residency training.1 The initial iteration specifically focused on the total and consecutive numbers of duty hours worked by trainees. These limitations began a new era of shift work in internal medicine residency training. With decreases in housestaff admitting capacity, clinical work has frequently been offloaded to non‐teaching or attending‐only services, increasing the demand for hospitalists to fill the void in physician‐staffed care in the hospital.2, 3 Since the implementation of the 2003 ACGME guidelines and a growing focus on patient safety, there has been increased study of, and call for, oversight of trainees in medicine; among these was the 2008 Institute of Medicine report,4 calling for 24/7 attending‐level supervision. The updated ACGME requirements,5 effective July 1, 2011, mandate enhanced on‐site supervision of trainee physicians. These new regulations not only define varying levels of supervision for trainees, including direct supervision with the physical presence of a supervisor and the degree of availability of said supervisor, they also describe ensuring the quality of supervision provided.5 While continuous attending‐level supervision is not yet mandated, many residency programs look to their academic hospitalists to fill the supervisory void, particularly at night. However, what specific roles hospitalists play in the nighttime supervision of trainees or the impact of this supervision remains unclear. To date, no study has examined a broad sample of hospitalist programs in teaching hospitals and the types of resident oversight they provide. We aimed to describe the current state of academic hospitalists in the clinical supervision of housestaff, specifically during the overnight period, and hospitalist perceptions of how the new ACGME requirements would impact traineehospitalist interactions.

METHODS

The Housestaff Oversight Subcommittee, a working group of the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force, surveyed a sample of academic hospitalist program leaders to assess the current status of trainee supervision performed by hospitalists. Programs were considered academic if they were located in the primary hospital of a residency that participates in the National Resident Matching Program for Internal Medicine. To obtain a broad geographic spectrum of academic hospitalist programs, all programs, both university and community‐based, in 4 states and 2 metropolitan regions were sampled: Washington, Oregon, Texas, Maryland, and the Philadelphia and Chicago metropolitan areas. Hospitalist program leaders were identified by members of the Taskforce using individual program websites and by querying departmental leadership at eligible teaching hospitals. Respondents were contacted by e‐mail for participation. None of the authors of the manuscript were participants in the survey.

The survey was developed by consensus of the working group after reviewing the salient literature and included additional questions queried to internal medicine program directors.6 The 19‐item SurveyMonkey instrument included questions about hospitalists' role in trainees' education and evaluation. A Likert‐type scale was used to assess perceptions regarding the impact of on‐site hospitalist supervision on trainee autonomy and hospitalist workload (1 = strongly disagree to 5 = strongly agree). Descriptive statistics were performed and, where appropriate, t test and Fisher's exact test were performed to identify associations between program characteristics and perceptions. Stata SE was used (STATA Corp, College Station, TX) for all statistical analysis.

RESULTS

The survey was sent to 47 individuals identified as likely hospitalist program leaders and completed by 41 individuals (87%). However, 7 respondents turned out not to be program leaders and were therefore excluded, resulting in a 72% (34/47) survey response rate.

The programs for which we did not obtain responses were similar to respondent programs, and did not include a larger proportion of community‐based programs or overrepresent a specific geographic region. Twenty‐five (73%) of the 34 hospitalist program leaders were male, with an average age of 44.3 years, and an average of 12 years post‐residency training (range, 530 years). They reported leading groups with an average of 18 full‐time equivalent (FTE) faculty (range, 350 persons).

DISCUSSION

This study provides important information about how academic hospitalists currently contribute to the supervision of internal medicine residents. While academic hospitalist groups frequently have faculty providing clinical care on‐site at night, and often hospitalists provide overnight supervision of internal medicine trainees, formal supervision of trainees is not uniform, and few hospitalists groups have a mechanism to provide training or faculty development on how to effectively supervise resident trainees. Hospitalist leaders expressed concerns that creating additional formal overnight supervisory responsibilities may add to an already burdened overnight hospitalist. Formalizing this supervisory role, including explicit role definitions and faculty training for trainee supervision, is necessary.

Though our sample size is small, we captured a diverse geographic range of both university and community‐based academic hospitalist programs by surveying group leaders in several distinct regions. We are unable to comment on differences between responding and non‐responding hospitalist programs, but there does not appear to be a systematic difference between these groups.

Our findings are consistent with work describing a lack of structured conceptual frameworks in effectively supervising trainees,7, 8 and also, at times, nebulous expectations for hospitalist faculty. We found that the existence of a formal supervisory policy within the associated residency program, as well as defined roles for hospitalists, increases the likelihood of positive perceptions of the new ACGME supervisory recommendations. However, the existence of these requirements does not mean that all programs are capable of following them. While additional discussion is required to best delineate a formal overnight hospitalist role in trainee supervision, clearly defining expectations for both faculty and trainees, and their interactions, may alleviate the struggles that exist in programs with ill‐defined roles for hospitalist faculty supervision. While faculty duty hours standards do not exist, additional duties of nighttime coverage for hospitalists suggests that close attention should be paid to burn‐out.9 Faculty development on nighttime supervision and teaching may help maximize both learning and patient care efficiency, and provide a framework for this often unstructured educational time.

In the era of Accountable Care Organizations (ACO) and need to improve transitions of care, diagnosis and management of diseases across the continuum from ambulatory to inpatient care remains of paramount importance.1, 2 Opportunities for screening have typically been viewed as the responsibility of the ambulatory primary care provider (PCP), yet in an ACO model, patients who present more frequently to a hospital as opposed to a clinic are still the responsibility of the ACO, and therefore opportunistic screening for certain diseases by hospitalists and other inpatient providers is a possibility that may merit further investigation. This opportunistic rationale has already been used to advocate for pneumococcal and influenza vaccination prior to discharge in hospitalized patients, but has not been well investigated in chronic disease screening.35

Diabetes mellitus is a disease that has reached epidemic proportions. National Health and Nutrition Examination Survey (NHANES) data documented the ambulatory prevalence of diabetes mellitus (DM) in adults 20 years of age in the United States to be 12.9%.6 However, the most significant health crisis may be that 40% of these adult patients with diabetes are unaware of their diagnosis.6 In other words, 5.1% of all adults 20 years of age or older in this country have undiagnosed diabetes.6, 7 As diabetes is a disease where clinical manifestations are often preceded by a prolonged asymptomatic period, screening with either of the preferred diagnostic tests, fasting blood glucose (FBG) or hemoglobin A_1C (Hgb A_1C), is required to make a new diagnosis.79

Diagnosis of hyperglycemia is important so that appropriate glycemic control can be achieved, and preventive care and risk factor modification can be initiated, including screening and treatment of hypertension, hyperlipidemia, retinopathy, nephropathy, and other comorbid conditions.7, 9 As glycemic control cannot be achieved in patients who remain undiagnosed, screening may play a role in preventing long‐term complications of diabetes.7 Awareness of the prediabetic states impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) is also important because lifestyle modification may delay or prevent the progression to diabetes and its associated complications, such as cardiovascular disease, retinopathy, and nephropathy.10, 11 In the inpatient setting, undiagnosed elevation of Hgb A_1C in the diabetes or prediabetes range has been shown to increase cost and length of stay in some spine surgery patients compared to patients with known diabetes.12

Virtually every inpatient has at least 1 glucose value drawn during hospitalization as part of a chemistry panel, many of which are fasting, or NPO (nil per os, meaning nothing by mouth), by virtue of clinical condition or anticipated procedure. Provided the preoperative state in an elective surgery patient is not taxing enough to induce stress hyperglycemia,1315 this typically fasting time may provide an easy and excellent diabetes screening opportunity to not only risk stratify for the inpatient stay, but to diagnose diabetes that will initiate lifelong care and prevention, provided information learned during hospitalization is conveyed to the PCP at discharge. While prior studies1618 have measured preoperative glucose as a means to risk stratify and predict undiagnosed diabetes, none of these analyses have obtained a second glycemic test (either FBG or Hgb A_1C) as required by the American Diabetes Association (ADA) to make a diagnosis of diabetes. Lack of a confirmatory glycemic test in the existing literature also leaves uncertainty in reproducibility and validity of the preoperative glucose as a risk‐stratification tool, as it is not certain that it is truly unstressed. Finally, studies to date have not evaluated or controlled for factors that could contribute to undiagnosed diabetes, such as health insurance and access to primary care.

To investigate the prevalence of undiagnosed diabetes and prediabetes in a hospitalized population, and to pilot the concept of screening in the inpatient preoperative setting, we performed a prospective analysis of adult orthopedic patients presenting for elective hip, knee, and spine surgery at a large Midwestern academic medical center from December 1, 2007 to November 30, 2008. Our primary objective was to determine the feasibility of preoperative testing in finding the prevalence of undiagnosed diabetes and prediabetes in an insured, inpatient population with access to prior preventive care. In addition, we investigated systems issues related to the general concept of inpatient screening, including assessment of whether providers recognized hyperglycemic patients in the hospital once tested, or conveyed test information to PCPs at discharge.

METHODS

The University of Wisconsin Institutional Review Board approved this prospective observational cohort study. All patients aged 18 years scheduled for elective total knee or hip arthroplasty, or elective lumbar decompression and/or fusion, presenting for preoperative appointment from December 1, 2007 to November 30, 2008, were invited to participate. Pregnant patients, and patients unable to give consent were excluded. Patients with hemolytic processes or on new regimens of oral or intravenous steroids within 7 days of surgery were also excluded. Patients on chronic oral, inhaled, intranasal, or topical steroids were included.

Preoperative Clinic Visit (Visit 1)

Patients who consented to participate had basic measures recorded, including height, weight, age, ethnicity, sex, date of surgery, and type of surgery. Patients then completed a questionnaire regarding previous history of diabetes and prediabetes (IFG or IGT), and personal history of other ADA‐designated risk factors9 to prompt diabetes screening, including gestational diabetes, hypertension, hyperlipidemia, vascular disease, and physical inactivity, as measured by the University of California, Los Angeles (UCLA) score.19 Patient self‐reported diagnosis of DM or prediabetes was compared to anesthesia preoperative assessment for confirmation. Finally, insurance status and most recent visit to a PCP were recorded (Figure 1).

Figure 1

RESULTS

A total of 302 patients met inclusion criteria and enrolled in the study. Of these patients, 27 (8.9%) were not included in final analysis due to incomplete preoperative labs (7 patients, 2.3%), lack of follow‐up (11 patients, 3.6%), withdrawal of consent (5 patients, 1.7%), or not having surgery (4 patients, 1.3%). Of the remaining 275, 54% were female. The mean patient age was 60.3 years, and 88% (243/275) of patients had a body mass index (BMI) 25 kg/m², indicating overweight or obese. All of the patients (100%) had healthcare insurance; 97% reported having a primary care provider, with 96.6% of patients stating that they had seen a primary provider within the year prior to surgery (Table 1).

Of the 275 patients, 50 (18%) had Known Diabetes/Prediabetes, 67 (24%) were given a new diagnosis of DM or IFG (New Diabetes/Prediabetes), and the remaining 158 (58%) were classified as Normoglycemia (Table 2). The sum of Known Diabetes/Prediabetes (50) and New Diabetes/Prediabetes (67) equaled the true inpatient prevalence of DM and IFG (117/275, 43%). Of the Known Diabetes/Prediabetes patients, 33/50 (66%) had DM and 17/50 (34%) had IFG. Of those with New Diabetes/Prediabetes, 8/67 (12%) had DM range values, with the remaining 59/67 (88%) in IFG range.

Patients with New Diabetes/Prediabetes had a higher preoperative Visit 2 glucose (mean [standard deviation], 110.79 [8.69] and 96.04 [9.10], P < 0.0001) and Hgb A_1C (5.80 [0.39] and 5.45 [0.36], P < 0.0001) compared to Normoglycemia. A subset of the Normoglycemia patients (38/158, 24%), had an elevated preoperative Visit 2 glucose, but a normal (<100 mg/dL) second confirmatory Visit 3 glucose, and therefore did not have New Diabetes/Prediabetes. New Diabetes/Prediabetes was also significantly different from this particular Normoglycemia subset in both FBG (110.79 [8.69] and 107.26 [8.69], P = 0.048) and Hgb A_1C (5.80 [0.39] and 5.54 [0.35], P = 0.001) (Table 2). Preoperative Visit 2 FBG of 100 mg/dL predicted Visit 3 FBG 100 mg/dL 64% of the time. Having both preoperative Visit 2 FBG 100 mg/dL and Hgb A_1C 5.7 (the ADA‐determined level for prediabetes),3 predicted Visit 3 FBG 100 mg/dL 72% of the time.

Patients with New Diabetes/Prediabetes were slightly older than Normoglycemia patients (62.37 [9.70] vs 58.08 [12.01], P = 0.0054), meeting the ADA diabetes screening age of 45 significantly more often than Normoglycemia patients (100% [67] vs 84% [132], P < 0.001). The groups otherwise did not differ in the incidence of other ADA‐defined risk factors9 (Table 3). Patients with New Diabetes/Prediabetes were less likely to report having seen their PCP within 6 months prior to surgery compared to their Normoglycemia counterparts (82% [54] vs 91% [141], P = 0.046), although this difference disappeared by 1 year (94% vs 96%). Finally, there was no increase in the number of point‐of‐care (POC) glucose tests ordered, or mention of hyperglycemia on discharge summaries in the New Diabetes/Prediabetes group (Table 3).

DISCUSSION AND CONCLUSION

The main finding of this study is that in an insured, elective orthopedic population with access to primary care, 24% of patients had unrecognized IFG or DM on the basis of 2 fasting blood glucose values. Remarkably, this statistic likely represents a best‐case scenario, as the percent of undiagnosed patients is likely higher in uninsured patients,20 those without primary care visits, and those hospitalized for emergent or urgent reasons who, by definition, did not have an ambulatory preoperative evaluation, and who may also have greater severity of illness at baseline. With over 1,053,000 total knee and hip operations done in the United States each year, opportunistic screening of this population alone could identify 252,720 patients with prediabetes or diabetes who might otherwise remain undiagnosed.21 Even more significant, with at least 70 million patients undergoing ambulatory or inpatient procedures each year, if even a quarter of these procedures were elective adult lower acuity surgeries allowing for easy preoperative testing, over 4 million cases of DM and IFG could be found each year using this process.21, 22 These numbers demonstrate the need to investigate new and novel screening opportunities, such as in hospitalized patients. These statistics also demonstrate the need for all inpatient providers to be aware of undiagnosed diabetes and prediabetes in their patients, and confirm recommendations of the Endocrine Society to obtain a blood glucose for all patients on admission, and measure Hgb A_1C in all hyperglycemic or diabetic inpatients if not performed in the preceding 23 months.23

Diagnosis of DM has historically been difficult to make in the hospital setting. The primary diagnostic test, FBG, may be elevated in the setting of counter‐regulatory hormone surge and inflammatory stress response, and its use has been discouraged in the acute care setting.14, 15, 24 While not affected by stress, Hgb A_1C, endorsed in 2010 by the ADA for diagnosis of DM,8 may still be unreliable in the setting of blood loss, transfusion, hemolysis, and other factors common during surgery and hospitalization.9, 25 However, we found that 64% of patients with elevated (100 mg/dL) blood glucose at the time of pre‐anesthesia evaluation did have persistently elevated blood glucose at 68 week follow‐up. This suggests that the preoperative glucose is unstressed, and may be a rapid, reasonably reliable indicator of patients needing ambulatory follow‐up to confirm DM or prediabetes. This may also provide perioperative risk stratification if glycemic history is unknown. As many fasting, preoperative patients have routine chemistry panels ordered already, the simple glucose included in such panels may prove to be the most useful diabetes test for anesthesiologists, surgeons, hospitalists, and other inpatient providers. Our data suggests that Hgb A_1C 5.7, the ADA‐suggested IFG/prediabetes cut point,9 can also be used in combination with FBG 100 to predict persistent hyperglycemia.

This study also revealed several significant systems issues that merit attention if opportunistic inpatient screening or preventive care is to be successful in a shared responsibility ACO system. Most importantly, none of our patients with elevated preoperative blood glucose had these results conveyed to their primary care provider at discharge, revealing both a need for improved transitions in care and development of formal ACO structure if inpatient or preoperative screening is to be successful. Second, our study also showed that providers did not change plan of care for patients without known DM or IFG and preoperative elevated glucose. None of these patients had point‐of‐care glucose checks ordered while in the hospital, demonstrating that previously undiagnosed dysglycemic patients receive different in‐hospital care compared to patients with known DM. While it is possible that providers consciously decided not to monitor patients with mild hyperglycemia, consistent with inpatient guidelines recommending glycemic targets of <180 mg/dL for general care patients,20 it is more likely that there was lack of recognition of hyperglycemia in these patients without prior DM or IFG, as has been demonstrated previously.26 Inpatient providers should be informed of, and encouraged to, follow Endocrine Society recommendations to monitor POC glucose in patients with hyperglycemia (>140 mg/dL) for at least 2448 hours.23

It is important to state that controversy exists regarding which patients should be screened for diabetes. The United States Preventive Services Task Force (USPSTF) recommends screening adult patients only if they have hypertension.27 The ADA recommends screening all patients 45 years of age and older, and younger, overweight patients with at least 1 additional risk factor.9 We have previously shown that using USPSTF guidelines misses 33.1% of cases of DM compared to the ADA standard.28 As such, our institution and the Wisconsin State Diabetes Screening Guidelines mirror the ADA guidelines.29, 30 In the present study, 91% were aged 45 and older, and 88% were overweight, so nearly everyone in our study met our state and institution guidelines for diabetes screening. However, this might not be the case at all institutions if USPSTF guidelines were instead followed.

A limitation of the present study was that a selection bias of subjects could have occurred by both patients and providers, as less healthy patients with higher surgical risk may not have been candidates for surgery as often as lower‐risk patients. While entirely appropriate to maximize safety for elective surgery patients, this may in part explain the lower Hgb A_1C (6.53 [0.14]) in our Known Diabetes/Prediabetes group, and lower range of blood glucose values in our New Diabetes/Prediabetes patients, with the majority being in the prediabetes range. However, this limitation also allows for the conclusion that any patient, regardless of perceived good health and primary care visits, may still have undiagnosed DM or IFG.

In summary, this study strongly supports the practice of screening obligate fasting patients to reduce the prevalence of undiagnosed diabetes. Despite the fact that our patients had insurance and recent primary care visits, nearly one‐quarter of individuals had previously unrecognized dysglycemia. This study also revealed systems issues, including the need for improved care transitions and development of a structure for shared responsibility in an ACO system, that need to be addressed if screening initiatives are to be effective in the hospital setting. Future studies will be needed to determine if other opportunistic screening tests have case‐finding potential, and further, how transitions processes can be improved to ensure that knowledge gained in the hospital is conveyed to the ambulatory setting.

In the era of Accountable Care Organizations (ACO) and need to improve transitions of care, diagnosis and management of diseases across the continuum from ambulatory to inpatient care remains of paramount importance.1, 2 Opportunities for screening have typically been viewed as the responsibility of the ambulatory primary care provider (PCP), yet in an ACO model, patients who present more frequently to a hospital as opposed to a clinic are still the responsibility of the ACO, and therefore opportunistic screening for certain diseases by hospitalists and other inpatient providers is a possibility that may merit further investigation. This opportunistic rationale has already been used to advocate for pneumococcal and influenza vaccination prior to discharge in hospitalized patients, but has not been well investigated in chronic disease screening.35

Diabetes mellitus is a disease that has reached epidemic proportions. National Health and Nutrition Examination Survey (NHANES) data documented the ambulatory prevalence of diabetes mellitus (DM) in adults 20 years of age in the United States to be 12.9%.6 However, the most significant health crisis may be that 40% of these adult patients with diabetes are unaware of their diagnosis.6 In other words, 5.1% of all adults 20 years of age or older in this country have undiagnosed diabetes.6, 7 As diabetes is a disease where clinical manifestations are often preceded by a prolonged asymptomatic period, screening with either of the preferred diagnostic tests, fasting blood glucose (FBG) or hemoglobin A_1C (Hgb A_1C), is required to make a new diagnosis.79

Diagnosis of hyperglycemia is important so that appropriate glycemic control can be achieved, and preventive care and risk factor modification can be initiated, including screening and treatment of hypertension, hyperlipidemia, retinopathy, nephropathy, and other comorbid conditions.7, 9 As glycemic control cannot be achieved in patients who remain undiagnosed, screening may play a role in preventing long‐term complications of diabetes.7 Awareness of the prediabetic states impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) is also important because lifestyle modification may delay or prevent the progression to diabetes and its associated complications, such as cardiovascular disease, retinopathy, and nephropathy.10, 11 In the inpatient setting, undiagnosed elevation of Hgb A_1C in the diabetes or prediabetes range has been shown to increase cost and length of stay in some spine surgery patients compared to patients with known diabetes.12

Virtually every inpatient has at least 1 glucose value drawn during hospitalization as part of a chemistry panel, many of which are fasting, or NPO (nil per os, meaning nothing by mouth), by virtue of clinical condition or anticipated procedure. Provided the preoperative state in an elective surgery patient is not taxing enough to induce stress hyperglycemia,1315 this typically fasting time may provide an easy and excellent diabetes screening opportunity to not only risk stratify for the inpatient stay, but to diagnose diabetes that will initiate lifelong care and prevention, provided information learned during hospitalization is conveyed to the PCP at discharge. While prior studies1618 have measured preoperative glucose as a means to risk stratify and predict undiagnosed diabetes, none of these analyses have obtained a second glycemic test (either FBG or Hgb A_1C) as required by the American Diabetes Association (ADA) to make a diagnosis of diabetes. Lack of a confirmatory glycemic test in the existing literature also leaves uncertainty in reproducibility and validity of the preoperative glucose as a risk‐stratification tool, as it is not certain that it is truly unstressed. Finally, studies to date have not evaluated or controlled for factors that could contribute to undiagnosed diabetes, such as health insurance and access to primary care.

To investigate the prevalence of undiagnosed diabetes and prediabetes in a hospitalized population, and to pilot the concept of screening in the inpatient preoperative setting, we performed a prospective analysis of adult orthopedic patients presenting for elective hip, knee, and spine surgery at a large Midwestern academic medical center from December 1, 2007 to November 30, 2008. Our primary objective was to determine the feasibility of preoperative testing in finding the prevalence of undiagnosed diabetes and prediabetes in an insured, inpatient population with access to prior preventive care. In addition, we investigated systems issues related to the general concept of inpatient screening, including assessment of whether providers recognized hyperglycemic patients in the hospital once tested, or conveyed test information to PCPs at discharge.

METHODS

The University of Wisconsin Institutional Review Board approved this prospective observational cohort study. All patients aged 18 years scheduled for elective total knee or hip arthroplasty, or elective lumbar decompression and/or fusion, presenting for preoperative appointment from December 1, 2007 to November 30, 2008, were invited to participate. Pregnant patients, and patients unable to give consent were excluded. Patients with hemolytic processes or on new regimens of oral or intravenous steroids within 7 days of surgery were also excluded. Patients on chronic oral, inhaled, intranasal, or topical steroids were included.

Preoperative Clinic Visit (Visit 1)

Patients who consented to participate had basic measures recorded, including height, weight, age, ethnicity, sex, date of surgery, and type of surgery. Patients then completed a questionnaire regarding previous history of diabetes and prediabetes (IFG or IGT), and personal history of other ADA‐designated risk factors9 to prompt diabetes screening, including gestational diabetes, hypertension, hyperlipidemia, vascular disease, and physical inactivity, as measured by the University of California, Los Angeles (UCLA) score.19 Patient self‐reported diagnosis of DM or prediabetes was compared to anesthesia preoperative assessment for confirmation. Finally, insurance status and most recent visit to a PCP were recorded (Figure 1).

Figure 1

RESULTS

A total of 302 patients met inclusion criteria and enrolled in the study. Of these patients, 27 (8.9%) were not included in final analysis due to incomplete preoperative labs (7 patients, 2.3%), lack of follow‐up (11 patients, 3.6%), withdrawal of consent (5 patients, 1.7%), or not having surgery (4 patients, 1.3%). Of the remaining 275, 54% were female. The mean patient age was 60.3 years, and 88% (243/275) of patients had a body mass index (BMI) 25 kg/m², indicating overweight or obese. All of the patients (100%) had healthcare insurance; 97% reported having a primary care provider, with 96.6% of patients stating that they had seen a primary provider within the year prior to surgery (Table 1).

Of the 275 patients, 50 (18%) had Known Diabetes/Prediabetes, 67 (24%) were given a new diagnosis of DM or IFG (New Diabetes/Prediabetes), and the remaining 158 (58%) were classified as Normoglycemia (Table 2). The sum of Known Diabetes/Prediabetes (50) and New Diabetes/Prediabetes (67) equaled the true inpatient prevalence of DM and IFG (117/275, 43%). Of the Known Diabetes/Prediabetes patients, 33/50 (66%) had DM and 17/50 (34%) had IFG. Of those with New Diabetes/Prediabetes, 8/67 (12%) had DM range values, with the remaining 59/67 (88%) in IFG range.

Patients with New Diabetes/Prediabetes had a higher preoperative Visit 2 glucose (mean [standard deviation], 110.79 [8.69] and 96.04 [9.10], P < 0.0001) and Hgb A_1C (5.80 [0.39] and 5.45 [0.36], P < 0.0001) compared to Normoglycemia. A subset of the Normoglycemia patients (38/158, 24%), had an elevated preoperative Visit 2 glucose, but a normal (<100 mg/dL) second confirmatory Visit 3 glucose, and therefore did not have New Diabetes/Prediabetes. New Diabetes/Prediabetes was also significantly different from this particular Normoglycemia subset in both FBG (110.79 [8.69] and 107.26 [8.69], P = 0.048) and Hgb A_1C (5.80 [0.39] and 5.54 [0.35], P = 0.001) (Table 2). Preoperative Visit 2 FBG of 100 mg/dL predicted Visit 3 FBG 100 mg/dL 64% of the time. Having both preoperative Visit 2 FBG 100 mg/dL and Hgb A_1C 5.7 (the ADA‐determined level for prediabetes),3 predicted Visit 3 FBG 100 mg/dL 72% of the time.

Patients with New Diabetes/Prediabetes were slightly older than Normoglycemia patients (62.37 [9.70] vs 58.08 [12.01], P = 0.0054), meeting the ADA diabetes screening age of 45 significantly more often than Normoglycemia patients (100% [67] vs 84% [132], P < 0.001). The groups otherwise did not differ in the incidence of other ADA‐defined risk factors9 (Table 3). Patients with New Diabetes/Prediabetes were less likely to report having seen their PCP within 6 months prior to surgery compared to their Normoglycemia counterparts (82% [54] vs 91% [141], P = 0.046), although this difference disappeared by 1 year (94% vs 96%). Finally, there was no increase in the number of point‐of‐care (POC) glucose tests ordered, or mention of hyperglycemia on discharge summaries in the New Diabetes/Prediabetes group (Table 3).

DISCUSSION AND CONCLUSION

The main finding of this study is that in an insured, elective orthopedic population with access to primary care, 24% of patients had unrecognized IFG or DM on the basis of 2 fasting blood glucose values. Remarkably, this statistic likely represents a best‐case scenario, as the percent of undiagnosed patients is likely higher in uninsured patients,20 those without primary care visits, and those hospitalized for emergent or urgent reasons who, by definition, did not have an ambulatory preoperative evaluation, and who may also have greater severity of illness at baseline. With over 1,053,000 total knee and hip operations done in the United States each year, opportunistic screening of this population alone could identify 252,720 patients with prediabetes or diabetes who might otherwise remain undiagnosed.21 Even more significant, with at least 70 million patients undergoing ambulatory or inpatient procedures each year, if even a quarter of these procedures were elective adult lower acuity surgeries allowing for easy preoperative testing, over 4 million cases of DM and IFG could be found each year using this process.21, 22 These numbers demonstrate the need to investigate new and novel screening opportunities, such as in hospitalized patients. These statistics also demonstrate the need for all inpatient providers to be aware of undiagnosed diabetes and prediabetes in their patients, and confirm recommendations of the Endocrine Society to obtain a blood glucose for all patients on admission, and measure Hgb A_1C in all hyperglycemic or diabetic inpatients if not performed in the preceding 23 months.23

Diagnosis of DM has historically been difficult to make in the hospital setting. The primary diagnostic test, FBG, may be elevated in the setting of counter‐regulatory hormone surge and inflammatory stress response, and its use has been discouraged in the acute care setting.14, 15, 24 While not affected by stress, Hgb A_1C, endorsed in 2010 by the ADA for diagnosis of DM,8 may still be unreliable in the setting of blood loss, transfusion, hemolysis, and other factors common during surgery and hospitalization.9, 25 However, we found that 64% of patients with elevated (100 mg/dL) blood glucose at the time of pre‐anesthesia evaluation did have persistently elevated blood glucose at 68 week follow‐up. This suggests that the preoperative glucose is unstressed, and may be a rapid, reasonably reliable indicator of patients needing ambulatory follow‐up to confirm DM or prediabetes. This may also provide perioperative risk stratification if glycemic history is unknown. As many fasting, preoperative patients have routine chemistry panels ordered already, the simple glucose included in such panels may prove to be the most useful diabetes test for anesthesiologists, surgeons, hospitalists, and other inpatient providers. Our data suggests that Hgb A_1C 5.7, the ADA‐suggested IFG/prediabetes cut point,9 can also be used in combination with FBG 100 to predict persistent hyperglycemia.

This study also revealed several significant systems issues that merit attention if opportunistic inpatient screening or preventive care is to be successful in a shared responsibility ACO system. Most importantly, none of our patients with elevated preoperative blood glucose had these results conveyed to their primary care provider at discharge, revealing both a need for improved transitions in care and development of formal ACO structure if inpatient or preoperative screening is to be successful. Second, our study also showed that providers did not change plan of care for patients without known DM or IFG and preoperative elevated glucose. None of these patients had point‐of‐care glucose checks ordered while in the hospital, demonstrating that previously undiagnosed dysglycemic patients receive different in‐hospital care compared to patients with known DM. While it is possible that providers consciously decided not to monitor patients with mild hyperglycemia, consistent with inpatient guidelines recommending glycemic targets of <180 mg/dL for general care patients,20 it is more likely that there was lack of recognition of hyperglycemia in these patients without prior DM or IFG, as has been demonstrated previously.26 Inpatient providers should be informed of, and encouraged to, follow Endocrine Society recommendations to monitor POC glucose in patients with hyperglycemia (>140 mg/dL) for at least 2448 hours.23

It is important to state that controversy exists regarding which patients should be screened for diabetes. The United States Preventive Services Task Force (USPSTF) recommends screening adult patients only if they have hypertension.27 The ADA recommends screening all patients 45 years of age and older, and younger, overweight patients with at least 1 additional risk factor.9 We have previously shown that using USPSTF guidelines misses 33.1% of cases of DM compared to the ADA standard.28 As such, our institution and the Wisconsin State Diabetes Screening Guidelines mirror the ADA guidelines.29, 30 In the present study, 91% were aged 45 and older, and 88% were overweight, so nearly everyone in our study met our state and institution guidelines for diabetes screening. However, this might not be the case at all institutions if USPSTF guidelines were instead followed.

A limitation of the present study was that a selection bias of subjects could have occurred by both patients and providers, as less healthy patients with higher surgical risk may not have been candidates for surgery as often as lower‐risk patients. While entirely appropriate to maximize safety for elective surgery patients, this may in part explain the lower Hgb A_1C (6.53 [0.14]) in our Known Diabetes/Prediabetes group, and lower range of blood glucose values in our New Diabetes/Prediabetes patients, with the majority being in the prediabetes range. However, this limitation also allows for the conclusion that any patient, regardless of perceived good health and primary care visits, may still have undiagnosed DM or IFG.

In summary, this study strongly supports the practice of screening obligate fasting patients to reduce the prevalence of undiagnosed diabetes. Despite the fact that our patients had insurance and recent primary care visits, nearly one‐quarter of individuals had previously unrecognized dysglycemia. This study also revealed systems issues, including the need for improved care transitions and development of a structure for shared responsibility in an ACO system, that need to be addressed if screening initiatives are to be effective in the hospital setting. Future studies will be needed to determine if other opportunistic screening tests have case‐finding potential, and further, how transitions processes can be improved to ensure that knowledge gained in the hospital is conveyed to the ambulatory setting.

In the era of Accountable Care Organizations (ACO) and need to improve transitions of care, diagnosis and management of diseases across the continuum from ambulatory to inpatient care remains of paramount importance.1, 2 Opportunities for screening have typically been viewed as the responsibility of the ambulatory primary care provider (PCP), yet in an ACO model, patients who present more frequently to a hospital as opposed to a clinic are still the responsibility of the ACO, and therefore opportunistic screening for certain diseases by hospitalists and other inpatient providers is a possibility that may merit further investigation. This opportunistic rationale has already been used to advocate for pneumococcal and influenza vaccination prior to discharge in hospitalized patients, but has not been well investigated in chronic disease screening.35

Diabetes mellitus is a disease that has reached epidemic proportions. National Health and Nutrition Examination Survey (NHANES) data documented the ambulatory prevalence of diabetes mellitus (DM) in adults 20 years of age in the United States to be 12.9%.6 However, the most significant health crisis may be that 40% of these adult patients with diabetes are unaware of their diagnosis.6 In other words, 5.1% of all adults 20 years of age or older in this country have undiagnosed diabetes.6, 7 As diabetes is a disease where clinical manifestations are often preceded by a prolonged asymptomatic period, screening with either of the preferred diagnostic tests, fasting blood glucose (FBG) or hemoglobin A_1C (Hgb A_1C), is required to make a new diagnosis.79

Diagnosis of hyperglycemia is important so that appropriate glycemic control can be achieved, and preventive care and risk factor modification can be initiated, including screening and treatment of hypertension, hyperlipidemia, retinopathy, nephropathy, and other comorbid conditions.7, 9 As glycemic control cannot be achieved in patients who remain undiagnosed, screening may play a role in preventing long‐term complications of diabetes.7 Awareness of the prediabetic states impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) is also important because lifestyle modification may delay or prevent the progression to diabetes and its associated complications, such as cardiovascular disease, retinopathy, and nephropathy.10, 11 In the inpatient setting, undiagnosed elevation of Hgb A_1C in the diabetes or prediabetes range has been shown to increase cost and length of stay in some spine surgery patients compared to patients with known diabetes.12

Virtually every inpatient has at least 1 glucose value drawn during hospitalization as part of a chemistry panel, many of which are fasting, or NPO (nil per os, meaning nothing by mouth), by virtue of clinical condition or anticipated procedure. Provided the preoperative state in an elective surgery patient is not taxing enough to induce stress hyperglycemia,1315 this typically fasting time may provide an easy and excellent diabetes screening opportunity to not only risk stratify for the inpatient stay, but to diagnose diabetes that will initiate lifelong care and prevention, provided information learned during hospitalization is conveyed to the PCP at discharge. While prior studies1618 have measured preoperative glucose as a means to risk stratify and predict undiagnosed diabetes, none of these analyses have obtained a second glycemic test (either FBG or Hgb A_1C) as required by the American Diabetes Association (ADA) to make a diagnosis of diabetes. Lack of a confirmatory glycemic test in the existing literature also leaves uncertainty in reproducibility and validity of the preoperative glucose as a risk‐stratification tool, as it is not certain that it is truly unstressed. Finally, studies to date have not evaluated or controlled for factors that could contribute to undiagnosed diabetes, such as health insurance and access to primary care.

To investigate the prevalence of undiagnosed diabetes and prediabetes in a hospitalized population, and to pilot the concept of screening in the inpatient preoperative setting, we performed a prospective analysis of adult orthopedic patients presenting for elective hip, knee, and spine surgery at a large Midwestern academic medical center from December 1, 2007 to November 30, 2008. Our primary objective was to determine the feasibility of preoperative testing in finding the prevalence of undiagnosed diabetes and prediabetes in an insured, inpatient population with access to prior preventive care. In addition, we investigated systems issues related to the general concept of inpatient screening, including assessment of whether providers recognized hyperglycemic patients in the hospital once tested, or conveyed test information to PCPs at discharge.

METHODS

The University of Wisconsin Institutional Review Board approved this prospective observational cohort study. All patients aged 18 years scheduled for elective total knee or hip arthroplasty, or elective lumbar decompression and/or fusion, presenting for preoperative appointment from December 1, 2007 to November 30, 2008, were invited to participate. Pregnant patients, and patients unable to give consent were excluded. Patients with hemolytic processes or on new regimens of oral or intravenous steroids within 7 days of surgery were also excluded. Patients on chronic oral, inhaled, intranasal, or topical steroids were included.

Preoperative Clinic Visit (Visit 1)

Patients who consented to participate had basic measures recorded, including height, weight, age, ethnicity, sex, date of surgery, and type of surgery. Patients then completed a questionnaire regarding previous history of diabetes and prediabetes (IFG or IGT), and personal history of other ADA‐designated risk factors9 to prompt diabetes screening, including gestational diabetes, hypertension, hyperlipidemia, vascular disease, and physical inactivity, as measured by the University of California, Los Angeles (UCLA) score.19 Patient self‐reported diagnosis of DM or prediabetes was compared to anesthesia preoperative assessment for confirmation. Finally, insurance status and most recent visit to a PCP were recorded (Figure 1).

Figure 1

RESULTS

A total of 302 patients met inclusion criteria and enrolled in the study. Of these patients, 27 (8.9%) were not included in final analysis due to incomplete preoperative labs (7 patients, 2.3%), lack of follow‐up (11 patients, 3.6%), withdrawal of consent (5 patients, 1.7%), or not having surgery (4 patients, 1.3%). Of the remaining 275, 54% were female. The mean patient age was 60.3 years, and 88% (243/275) of patients had a body mass index (BMI) 25 kg/m², indicating overweight or obese. All of the patients (100%) had healthcare insurance; 97% reported having a primary care provider, with 96.6% of patients stating that they had seen a primary provider within the year prior to surgery (Table 1).

Of the 275 patients, 50 (18%) had Known Diabetes/Prediabetes, 67 (24%) were given a new diagnosis of DM or IFG (New Diabetes/Prediabetes), and the remaining 158 (58%) were classified as Normoglycemia (Table 2). The sum of Known Diabetes/Prediabetes (50) and New Diabetes/Prediabetes (67) equaled the true inpatient prevalence of DM and IFG (117/275, 43%). Of the Known Diabetes/Prediabetes patients, 33/50 (66%) had DM and 17/50 (34%) had IFG. Of those with New Diabetes/Prediabetes, 8/67 (12%) had DM range values, with the remaining 59/67 (88%) in IFG range.

Patients with New Diabetes/Prediabetes had a higher preoperative Visit 2 glucose (mean [standard deviation], 110.79 [8.69] and 96.04 [9.10], P < 0.0001) and Hgb A_1C (5.80 [0.39] and 5.45 [0.36], P < 0.0001) compared to Normoglycemia. A subset of the Normoglycemia patients (38/158, 24%), had an elevated preoperative Visit 2 glucose, but a normal (<100 mg/dL) second confirmatory Visit 3 glucose, and therefore did not have New Diabetes/Prediabetes. New Diabetes/Prediabetes was also significantly different from this particular Normoglycemia subset in both FBG (110.79 [8.69] and 107.26 [8.69], P = 0.048) and Hgb A_1C (5.80 [0.39] and 5.54 [0.35], P = 0.001) (Table 2). Preoperative Visit 2 FBG of 100 mg/dL predicted Visit 3 FBG 100 mg/dL 64% of the time. Having both preoperative Visit 2 FBG 100 mg/dL and Hgb A_1C 5.7 (the ADA‐determined level for prediabetes),3 predicted Visit 3 FBG 100 mg/dL 72% of the time.

Patients with New Diabetes/Prediabetes were slightly older than Normoglycemia patients (62.37 [9.70] vs 58.08 [12.01], P = 0.0054), meeting the ADA diabetes screening age of 45 significantly more often than Normoglycemia patients (100% [67] vs 84% [132], P < 0.001). The groups otherwise did not differ in the incidence of other ADA‐defined risk factors9 (Table 3). Patients with New Diabetes/Prediabetes were less likely to report having seen their PCP within 6 months prior to surgery compared to their Normoglycemia counterparts (82% [54] vs 91% [141], P = 0.046), although this difference disappeared by 1 year (94% vs 96%). Finally, there was no increase in the number of point‐of‐care (POC) glucose tests ordered, or mention of hyperglycemia on discharge summaries in the New Diabetes/Prediabetes group (Table 3).

DISCUSSION AND CONCLUSION

The main finding of this study is that in an insured, elective orthopedic population with access to primary care, 24% of patients had unrecognized IFG or DM on the basis of 2 fasting blood glucose values. Remarkably, this statistic likely represents a best‐case scenario, as the percent of undiagnosed patients is likely higher in uninsured patients,20 those without primary care visits, and those hospitalized for emergent or urgent reasons who, by definition, did not have an ambulatory preoperative evaluation, and who may also have greater severity of illness at baseline. With over 1,053,000 total knee and hip operations done in the United States each year, opportunistic screening of this population alone could identify 252,720 patients with prediabetes or diabetes who might otherwise remain undiagnosed.21 Even more significant, with at least 70 million patients undergoing ambulatory or inpatient procedures each year, if even a quarter of these procedures were elective adult lower acuity surgeries allowing for easy preoperative testing, over 4 million cases of DM and IFG could be found each year using this process.21, 22 These numbers demonstrate the need to investigate new and novel screening opportunities, such as in hospitalized patients. These statistics also demonstrate the need for all inpatient providers to be aware of undiagnosed diabetes and prediabetes in their patients, and confirm recommendations of the Endocrine Society to obtain a blood glucose for all patients on admission, and measure Hgb A_1C in all hyperglycemic or diabetic inpatients if not performed in the preceding 23 months.23

Diagnosis of DM has historically been difficult to make in the hospital setting. The primary diagnostic test, FBG, may be elevated in the setting of counter‐regulatory hormone surge and inflammatory stress response, and its use has been discouraged in the acute care setting.14, 15, 24 While not affected by stress, Hgb A_1C, endorsed in 2010 by the ADA for diagnosis of DM,8 may still be unreliable in the setting of blood loss, transfusion, hemolysis, and other factors common during surgery and hospitalization.9, 25 However, we found that 64% of patients with elevated (100 mg/dL) blood glucose at the time of pre‐anesthesia evaluation did have persistently elevated blood glucose at 68 week follow‐up. This suggests that the preoperative glucose is unstressed, and may be a rapid, reasonably reliable indicator of patients needing ambulatory follow‐up to confirm DM or prediabetes. This may also provide perioperative risk stratification if glycemic history is unknown. As many fasting, preoperative patients have routine chemistry panels ordered already, the simple glucose included in such panels may prove to be the most useful diabetes test for anesthesiologists, surgeons, hospitalists, and other inpatient providers. Our data suggests that Hgb A_1C 5.7, the ADA‐suggested IFG/prediabetes cut point,9 can also be used in combination with FBG 100 to predict persistent hyperglycemia.

This study also revealed several significant systems issues that merit attention if opportunistic inpatient screening or preventive care is to be successful in a shared responsibility ACO system. Most importantly, none of our patients with elevated preoperative blood glucose had these results conveyed to their primary care provider at discharge, revealing both a need for improved transitions in care and development of formal ACO structure if inpatient or preoperative screening is to be successful. Second, our study also showed that providers did not change plan of care for patients without known DM or IFG and preoperative elevated glucose. None of these patients had point‐of‐care glucose checks ordered while in the hospital, demonstrating that previously undiagnosed dysglycemic patients receive different in‐hospital care compared to patients with known DM. While it is possible that providers consciously decided not to monitor patients with mild hyperglycemia, consistent with inpatient guidelines recommending glycemic targets of <180 mg/dL for general care patients,20 it is more likely that there was lack of recognition of hyperglycemia in these patients without prior DM or IFG, as has been demonstrated previously.26 Inpatient providers should be informed of, and encouraged to, follow Endocrine Society recommendations to monitor POC glucose in patients with hyperglycemia (>140 mg/dL) for at least 2448 hours.23

It is important to state that controversy exists regarding which patients should be screened for diabetes. The United States Preventive Services Task Force (USPSTF) recommends screening adult patients only if they have hypertension.27 The ADA recommends screening all patients 45 years of age and older, and younger, overweight patients with at least 1 additional risk factor.9 We have previously shown that using USPSTF guidelines misses 33.1% of cases of DM compared to the ADA standard.28 As such, our institution and the Wisconsin State Diabetes Screening Guidelines mirror the ADA guidelines.29, 30 In the present study, 91% were aged 45 and older, and 88% were overweight, so nearly everyone in our study met our state and institution guidelines for diabetes screening. However, this might not be the case at all institutions if USPSTF guidelines were instead followed.

A limitation of the present study was that a selection bias of subjects could have occurred by both patients and providers, as less healthy patients with higher surgical risk may not have been candidates for surgery as often as lower‐risk patients. While entirely appropriate to maximize safety for elective surgery patients, this may in part explain the lower Hgb A_1C (6.53 [0.14]) in our Known Diabetes/Prediabetes group, and lower range of blood glucose values in our New Diabetes/Prediabetes patients, with the majority being in the prediabetes range. However, this limitation also allows for the conclusion that any patient, regardless of perceived good health and primary care visits, may still have undiagnosed DM or IFG.

In summary, this study strongly supports the practice of screening obligate fasting patients to reduce the prevalence of undiagnosed diabetes. Despite the fact that our patients had insurance and recent primary care visits, nearly one‐quarter of individuals had previously unrecognized dysglycemia. This study also revealed systems issues, including the need for improved care transitions and development of a structure for shared responsibility in an ACO system, that need to be addressed if screening initiatives are to be effective in the hospital setting. Future studies will be needed to determine if other opportunistic screening tests have case‐finding potential, and further, how transitions processes can be improved to ensure that knowledge gained in the hospital is conveyed to the ambulatory setting.

Over 14% of all patients hospitalized in the United States are readmitted within 30 days of discharge.1 Numerous studies have used administrative data in order to identify clinical and operational predictors of readmission. However, few studies have explored patients' perspectives on readmission.27 As a result, we know little about potentially modifiable challenges which patients face during the transition from hospital to home. Lack of understanding of the patient perspective has hampered the ability of hospitals to create interventions which address these underlying causes of readmissions.

Patients with low socioeconomic status (SES) are up to 43% more likely to require readmission than their higher‐SES counterparts,8, 9 and qualitative data has described unique challenges faced by low‐SES patients during transition.2 Our objectives were to understand the transition experiences of readmitted patients and to compare these experiences across SES and diagnostic categories.

METHODS

RESULTS

DISCUSSION

Several findings from this study are of interest to practicing hospitalists or hospital administrators. First, of the issues to which patients most commonly attributed readmission, lack of discharge preparedness is the only one which occurs during index hospitalization; in order to address most transition challenges, hospitals must think beyond their walls. By penalizing hospitals for excess rates of readmission, The Hospital Readmission Reduction Program (HRRP) will effectively hold hospitals accountable for addressing issues which occur in patients' homes and communities.17 Hospitals that have robust partnerships with community pharmacies, social service agencies, and PCPs may have the most influence on these issues and the most success in reducing readmissions. Second, consistent with other literature describing increased rates of readmission with enhanced PCP follow‐up,18 our findings demonstrate that PCPs often refer their patients to the emergency room for readmission. This suggests that PCP follow‐up, while perhaps essential for patient care, may not necessarily reduce readmissions and may actually facilitate readmission. Third, this study describes underlying reasons for patient nonadherence with discharge medications: side effects, cost, and transportation. Targeted interventions to improve adherence may include floor‐based pharmacists who counsel on side effects, determine co‐pays prior to discharge, and encourage patients to fill prescriptions from the hospital pharmacy to avoid transportation barriers.

Finally, and perhaps most importantly, these data suggest that one transition experience does not fit all. Patients with low SES appear to have a distinct and challenging transition experience. Currently, there is an emphasis on tailoring transition interventions to specific disease populations, such as patients with congestive heart failure. Our study suggests that it may be more effective to tailor interventions for low‐SES patients across diagnostic category, helping these patients gain access to outpatient medical resources and address competing issues, such as food insecurity or substance abuse.

Our study has several limitations. First, the low survey response rate makes it susceptible to nonresponse bias. Second, survey administration by a member of the care team may have increased social desirability bias. Third, because it was important to the study team to incorporate our survey into hospital workflow, survey responses were recorded directly into the EMR which limited administrators to recording a yes response for each answer choice which the participant endorsed. Therefore, in our dataset, we are unable to distinguish a definite no from a missing response; however, the survey was short, making it unlikely that questions were skipped. Fourth, closed‐ended questions may have failed to capture the range of participant responses, although the inclusion of an open‐ended answer choice ameliorates this issue. Finally, we are unable to draw conclusions regarding association of survey responses with the risk of readmission, because this study was administered only to readmitted patients.

CONCLUSIONS

This report of patients' perspectives challenges many commonly held assumptions regarding readmission. Readmission reflects not only the quality of hospital care, but a variety of factors in patients' homes and communities. PCP follow‐up, while perhaps critical for patient care, may not be a panacea for reducing hospital readmissions. Targeted medication counseling focused on side effects, co‐pay, and medication delivery may address patients' underlying reasons for nonadherence. And most importantly, one transition experience does not fit all. Hospitalists and administrators must tailor interventions to address challenges reported by their patients, particularly those of low SES.

Over 14% of all patients hospitalized in the United States are readmitted within 30 days of discharge.1 Numerous studies have used administrative data in order to identify clinical and operational predictors of readmission. However, few studies have explored patients' perspectives on readmission.27 As a result, we know little about potentially modifiable challenges which patients face during the transition from hospital to home. Lack of understanding of the patient perspective has hampered the ability of hospitals to create interventions which address these underlying causes of readmissions.

Patients with low socioeconomic status (SES) are up to 43% more likely to require readmission than their higher‐SES counterparts,8, 9 and qualitative data has described unique challenges faced by low‐SES patients during transition.2 Our objectives were to understand the transition experiences of readmitted patients and to compare these experiences across SES and diagnostic categories.

METHODS

RESULTS

DISCUSSION

Several findings from this study are of interest to practicing hospitalists or hospital administrators. First, of the issues to which patients most commonly attributed readmission, lack of discharge preparedness is the only one which occurs during index hospitalization; in order to address most transition challenges, hospitals must think beyond their walls. By penalizing hospitals for excess rates of readmission, The Hospital Readmission Reduction Program (HRRP) will effectively hold hospitals accountable for addressing issues which occur in patients' homes and communities.17 Hospitals that have robust partnerships with community pharmacies, social service agencies, and PCPs may have the most influence on these issues and the most success in reducing readmissions. Second, consistent with other literature describing increased rates of readmission with enhanced PCP follow‐up,18 our findings demonstrate that PCPs often refer their patients to the emergency room for readmission. This suggests that PCP follow‐up, while perhaps essential for patient care, may not necessarily reduce readmissions and may actually facilitate readmission. Third, this study describes underlying reasons for patient nonadherence with discharge medications: side effects, cost, and transportation. Targeted interventions to improve adherence may include floor‐based pharmacists who counsel on side effects, determine co‐pays prior to discharge, and encourage patients to fill prescriptions from the hospital pharmacy to avoid transportation barriers.

Finally, and perhaps most importantly, these data suggest that one transition experience does not fit all. Patients with low SES appear to have a distinct and challenging transition experience. Currently, there is an emphasis on tailoring transition interventions to specific disease populations, such as patients with congestive heart failure. Our study suggests that it may be more effective to tailor interventions for low‐SES patients across diagnostic category, helping these patients gain access to outpatient medical resources and address competing issues, such as food insecurity or substance abuse.

Our study has several limitations. First, the low survey response rate makes it susceptible to nonresponse bias. Second, survey administration by a member of the care team may have increased social desirability bias. Third, because it was important to the study team to incorporate our survey into hospital workflow, survey responses were recorded directly into the EMR which limited administrators to recording a yes response for each answer choice which the participant endorsed. Therefore, in our dataset, we are unable to distinguish a definite no from a missing response; however, the survey was short, making it unlikely that questions were skipped. Fourth, closed‐ended questions may have failed to capture the range of participant responses, although the inclusion of an open‐ended answer choice ameliorates this issue. Finally, we are unable to draw conclusions regarding association of survey responses with the risk of readmission, because this study was administered only to readmitted patients.

CONCLUSIONS

This report of patients' perspectives challenges many commonly held assumptions regarding readmission. Readmission reflects not only the quality of hospital care, but a variety of factors in patients' homes and communities. PCP follow‐up, while perhaps critical for patient care, may not be a panacea for reducing hospital readmissions. Targeted medication counseling focused on side effects, co‐pay, and medication delivery may address patients' underlying reasons for nonadherence. And most importantly, one transition experience does not fit all. Hospitalists and administrators must tailor interventions to address challenges reported by their patients, particularly those of low SES.

Over 14% of all patients hospitalized in the United States are readmitted within 30 days of discharge.1 Numerous studies have used administrative data in order to identify clinical and operational predictors of readmission. However, few studies have explored patients' perspectives on readmission.27 As a result, we know little about potentially modifiable challenges which patients face during the transition from hospital to home. Lack of understanding of the patient perspective has hampered the ability of hospitals to create interventions which address these underlying causes of readmissions.

Patients with low socioeconomic status (SES) are up to 43% more likely to require readmission than their higher‐SES counterparts,8, 9 and qualitative data has described unique challenges faced by low‐SES patients during transition.2 Our objectives were to understand the transition experiences of readmitted patients and to compare these experiences across SES and diagnostic categories.

METHODS

RESULTS

DISCUSSION

Several findings from this study are of interest to practicing hospitalists or hospital administrators. First, of the issues to which patients most commonly attributed readmission, lack of discharge preparedness is the only one which occurs during index hospitalization; in order to address most transition challenges, hospitals must think beyond their walls. By penalizing hospitals for excess rates of readmission, The Hospital Readmission Reduction Program (HRRP) will effectively hold hospitals accountable for addressing issues which occur in patients' homes and communities.17 Hospitals that have robust partnerships with community pharmacies, social service agencies, and PCPs may have the most influence on these issues and the most success in reducing readmissions. Second, consistent with other literature describing increased rates of readmission with enhanced PCP follow‐up,18 our findings demonstrate that PCPs often refer their patients to the emergency room for readmission. This suggests that PCP follow‐up, while perhaps essential for patient care, may not necessarily reduce readmissions and may actually facilitate readmission. Third, this study describes underlying reasons for patient nonadherence with discharge medications: side effects, cost, and transportation. Targeted interventions to improve adherence may include floor‐based pharmacists who counsel on side effects, determine co‐pays prior to discharge, and encourage patients to fill prescriptions from the hospital pharmacy to avoid transportation barriers.

Finally, and perhaps most importantly, these data suggest that one transition experience does not fit all. Patients with low SES appear to have a distinct and challenging transition experience. Currently, there is an emphasis on tailoring transition interventions to specific disease populations, such as patients with congestive heart failure. Our study suggests that it may be more effective to tailor interventions for low‐SES patients across diagnostic category, helping these patients gain access to outpatient medical resources and address competing issues, such as food insecurity or substance abuse.

Our study has several limitations. First, the low survey response rate makes it susceptible to nonresponse bias. Second, survey administration by a member of the care team may have increased social desirability bias. Third, because it was important to the study team to incorporate our survey into hospital workflow, survey responses were recorded directly into the EMR which limited administrators to recording a yes response for each answer choice which the participant endorsed. Therefore, in our dataset, we are unable to distinguish a definite no from a missing response; however, the survey was short, making it unlikely that questions were skipped. Fourth, closed‐ended questions may have failed to capture the range of participant responses, although the inclusion of an open‐ended answer choice ameliorates this issue. Finally, we are unable to draw conclusions regarding association of survey responses with the risk of readmission, because this study was administered only to readmitted patients.

CONCLUSIONS

This report of patients' perspectives challenges many commonly held assumptions regarding readmission. Readmission reflects not only the quality of hospital care, but a variety of factors in patients' homes and communities. PCP follow‐up, while perhaps critical for patient care, may not be a panacea for reducing hospital readmissions. Targeted medication counseling focused on side effects, co‐pay, and medication delivery may address patients' underlying reasons for nonadherence. And most importantly, one transition experience does not fit all. Hospitalists and administrators must tailor interventions to address challenges reported by their patients, particularly those of low SES.