ANNSURG-D-15-01099; Total nos of Pages: 6;
ANNSURG-D-15-01099
ORIGINAL ARTICLE
Hospital Ownership of a Postacute Care Facility Influences Discharge Destinations After Emergent Surgery Zaid M. Abdelsattar, MD, MSc, y Andrew A. Gonzalez, MD, JD, MPH, z Samantha Hendren, MD, MPH, Scott E. Regenbogen, MD, MPH, and Sandra L. Wong, MD, MS
Objective: The aim of the study was to identify hospital characteristics associated with variation in patient disposition after emergent surgery. Summary background data: Colon resections in elderly patients are often done in emergent settings. Although these operations are known to be riskier, there are limited data regarding postoperative discharge destination. Methods: We evaluated Medicare beneficiaries who underwent emergent colectomy between 2008 and 2010. Using hierarchical logistic regression, we estimated patient and hospital-level risk-adjusted rates of nonhome discharges. Hospitals were stratified into quintiles based on their nonhome discharge rates. Generalized linear models were used to identify hospital structural characteristics associated with nonhome discharges (comparing discharge to skilled nursing facilities vs home with/without home health services). Results: Of the 122,604 patients surviving to discharge after emergent colectomy at 3012 hospitals, 46.7% were discharged to a nonhome destination. There was a wide variation in risk and reliability-adjusted nonhome discharge rates across hospitals (15% to 80%). Patients at hospitals in the highest quintile of nonhome discharge rates were more likely to have longer hospitalizations (15.1 vs 13.2; P < 0.001) and more complications (43.2% vs 34%; P < 0.001). On multivariable analysis, only hospital ownership of a skilled nursing facility (P < 0.001), teaching status (P ¼ 0.025), and low nurse-to-patient ratios (P ¼ 0.002) were associated with nonhome discharges. Conclusions: Nearly half of Medicare beneficiaries are discharged to a nonhome destination after emergent colectomy. Hospital ownership of a skilled nursing facility and low nurse-to-patient ratios are highly associated with nonhome discharges. This may signify the underlying financial incentives to preferentially utilize postacute care facilities under the traditional feefor-service payment model. Keywords: colectomy, emergency, Medicare, outcome, postacute care, surgery
(Ann Surg 2015;xx:xxx–xxx)
A
pproximately 115,000 older adults undergo a colectomy every year,1 and about one-third do so for a life-threatening condition. After surviving their emergent operation, many patients experience significant postoperative functional decline, which limits their ability to return home. Ideally, patients are discharged home
From the Center for Healthcare Outcomes and Policy, Department of Surgery, University of Michigan, Ann Arbor, MI; yDepartment of Surgery, Mayo Clinic, Rochester, MN; and zDepartment of Surgery, University of Illinois Chicago, Chicago, IL. Funding statement: Z.M.A. is supported by AHRQ T32 HS000053-23. S.L.W. is supported by AHRQ 1K08 HS20937-01 and American Cancer Society-RSG12-269-01-CPHPS. Disclosures: The authors have no disclosures to make. Reprints: Sandra L. Wong, MD, MS, FACS, University of Michigan, 1500 East Medical Center Drive/3310 Cancer Center, SPC 5932, Ann Arbor, MI 481095932. E-mail: [email protected]. Copyright ß 2015 Wolters Kluwer Health, Inc. All rights reserved. ISSN: 0003-4932/14/26105-0821 DOI: 10.1097/SLA.0000000000001498
Annals of Surgery Volume XX, Number X, Month 2015
postoperatively, with or without home health services. A significant number of patients are discharged to nonhome destinations (rehabilitation centers, short-term recovery hospitals, skilled nursing facilities, swing bed units, or other facilities) to regain functional independence. As a proposed patient-centered outcome,2 the discharge destination may also be a marker for the quality of postoperative recovery.3,4 Although recuperating in a postacute care facility may be essential for optimal recovery, utilizing such facilities is a significant source of variation in Medicare payments in the United States,5,6 and it amounts to over $62 billion/year of health care dollars.6 Under the traditional fee-for-service reimbursement model, Medicare pays hospitals and postacute care providers separately, without regard to the quality or efficiency through the episode of care. Patients’ discharge plans were therefore often made for financial rather than clinical indications,7,8 and may be more common if the hospitals and postacute care facilities operate under the same financial organization. To this end, Medicare has proposed several bundled-payment and shared-savings programs to provide financial incentives to better integrate acute and postacute care, and reduce inappropriate utilization. Although the variations in other short-term outcomes such as morbidity, failure to rescue, and mortality after surgery have gained significant attention,9–11 limited data exist to help in understanding the variation in discharge destinations. Recently, Sacks et al12 identified several patient characteristics, such as older age, poor functional status, and the occurrence of postoperative complications, as risk factors for nonhome discharges. At the hospital level, Balentine et al3 demonstrated that high-volume hospitals are more likely to discharge patients home after colorectal surgery compared to low-volume hospitals. However, these studies do not examine several important hospital characteristics or financial relationships and infrastructure that may directly influence the discharge destination after surgery. In this context, we used national data on Medicare beneficiaries undergoing emergent colectomy to investigate the extent to which nonhome discharge rates vary by hospital after accounting for patient factors. Then, we sought to better understand the hospital characteristics that may explain this variation in discharge destinations. Specifically, we hypothesized that hospitals with existing partnerships with skilled nursing facilities would be more likely to discharge patients to such facilities. Ultimately, the findings of this study may be relevant to stakeholders and payers as they aim to integrate acute and postacute care.
METHODS Data Source and Study Population We used a national data set from the 100% Medicare Provider Analysis and Review (MedPAR) files for the years 2008, 2009, and 2010. The Centers for Medicare and Medicaid Services (CMS) maintains this database using claims submitted for all fee-for-service www.annalsofsurgery.com | 1
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
ANNSURG-D-15-01099; Total nos of Pages: 6;
ANNSURG-D-15-01099
Abdelsattar et al
acute care hospitalizations of Medicare beneficiaries not enrolled in managed care plans. We identified patients between the ages of 65 and 99 years who underwent a colon resection, using International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes (17.32, 17.33, 17.34, 17.35, 17.36, 45.73, 45.74, 45.75, 45.76, 45.80, 45.81, 45.82, 45.83). We excluded patients who did not present to the hospital from home, those who had their operation in 2007, and those who were discharged from the hospital in December 2010. As the discharge destination was the main outcome in this study, we excluded patients not surviving to discharge. The type and priority of the beneficiary’s admission to a facility for the inpatient hospital stay based on MedPAR codes were used to exclude patients who underwent elective operations.
Patient and Hospital Characteristics We evaluated patient characteristics including age, sex, race, and coexisting conditions. Coexisting conditions were identified by the appropriate ICD-9-CM diagnosis codes and were defined using the Elixhauser method.13 The type of colon resection (segmental or total) and operative approach (laparoscopic or open) were defined using appropriate ICD-9-CM procedure codes. Postoperative complications were identified using specific previously validated ICD-9CM diagnosis codes.14 We used the 2010 American Hospital Association (AHA) Annual Survey Database to determine specific hospital characteristics shown to be associated with improved postsurgical outcomes, namely, teaching status, hospital bed size, average daily census, and nurse-to-patient ratios. Nurse-to-patient ratios (or number of nurses per 1000 patient-days) were calculated by a previously published and widely used definition of evaluating productive nursing hours described by Spetz et al,15 and is calculated from AHA data by the following formula: [Nursing full-time equivalents (FTEs) 1768]/adjusted patient-days. We also specifically obtained data on the hospital’s financial relationships with skilled nursing facilities from the AHA files. Hospitals with incomplete AHA survey data were excluded, and only short-term acute care hospitals were included in this study.
Statistical Analysis The primary outcome of this study was the hospital risk and reliability-adjusted nonhome discharge rate. We calculated point estimates for this outcome as follows. First, we used standard logistic regression to calculate an empirically derived linear risk score for nonhome discharges. This score was calculated through the use of a continuous variable for age, dichotomous variables for sex and nonwhite race, and indicator variables for preexisting Elixhauser comorbidities, the occurrence of any complication, the operation performed, and the surgical approach (open vs laparoscopic). Second, we used a hierarchical mixed-effects logistic regression model that was based on the patient risk score (described above) with the hospital identifier as the random effect. Hierarchical regression is an advanced statistical technique that leverages empirical Bayes theorem to directly model variation at the hospital level. As a result, hierarchical modeling yields stable estimates of both coefficients and standard errors, reduces statistical noise, and provides more robust estimates of hospital quality. Finally, we ranked the hospitals according to their risk and reliability-adjusted nonhome discharge rates, and stratified the hospitals into quintiles of nonhome discharge rates. To identify the association between the structural hospital characteristics and the adjusted nonhome discharge rates, we used a generalized linear model with the logit link function, which allows calculating the absolute contribution of each characteristic to the adjusted odds of nonhome discharges over the average rate. 2 | www.annalsofsurgery.com
Annals of Surgery Volume XX, Number X, Month 2015
The univariate comparisons of the risk and reliability-adjusted nonhome discharge rates were made between the 2 quintiles at the extremes of nonhome discharges (ie, the top and bottom 20% of hospitals). When adjusting for reliability, hospitals with the lowest caseloads were shrunk toward the mean, thereby filling the middle quintiles.16 Therefore, focusing on the 2 quintiles at the extremes compares the hospitals that contribute the most information. All statistical tests were two-tailed, and a P value 2 Preoperative comorbidities Cardiovascular Congestive heart failure Hypertension Peripheral vascular disease Valvular disease Pulmonary Pulmonary circulation disease Chronic pulmonary disease Neurological Paralysis Other neurological disorders Endocrine/metabolic Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Obesity Weight loss Gastrointestinal Liver disease Peptic ulcer disease Renal Renal failure Fluid and electrolyte disorders Hematologic Acquired immune deficiency syndrome Lymphoma Metastatic cancer Solid tumor without metastasis Rheumatoid arthritis/collagen diseases Coagulopathy Chronic blood loss anemia Deficiency anemia Other Alcohol abuse Depression Drug abuse Psychoses
Home (n ¼ 65,399) 75.2 þ 7 28495 (43.6) 55240 (84.5)
Nonhome (n ¼ 57,205) 79.7 þ 7.7 20640 (36.1) 49348 (86.3)
29904 (45.7) 27651 (42.3) 7844 (12) 9734 (14.9) 16921 (25.9) 24064 (36.8)
21352 (37.3) 26227 (45.8) 9626 (16.8) 3831 (6.7) 31179 (54.5) 20208 (35.3)
5408 (8.3) 33573 (51.3) 3251 (5) 2264 (3.5)
P
ANNSURG-D-15-01099
ORIGINAL ARTICLE
Hospital Ownership of a Postacute Care Facility Influences Discharge Destinations After Emergent Surgery Zaid M. Abdelsattar, MD, MSc, y Andrew A. Gonzalez, MD, JD, MPH, z Samantha Hendren, MD, MPH, Scott E. Regenbogen, MD, MPH, and Sandra L. Wong, MD, MS
Objective: The aim of the study was to identify hospital characteristics associated with variation in patient disposition after emergent surgery. Summary background data: Colon resections in elderly patients are often done in emergent settings. Although these operations are known to be riskier, there are limited data regarding postoperative discharge destination. Methods: We evaluated Medicare beneficiaries who underwent emergent colectomy between 2008 and 2010. Using hierarchical logistic regression, we estimated patient and hospital-level risk-adjusted rates of nonhome discharges. Hospitals were stratified into quintiles based on their nonhome discharge rates. Generalized linear models were used to identify hospital structural characteristics associated with nonhome discharges (comparing discharge to skilled nursing facilities vs home with/without home health services). Results: Of the 122,604 patients surviving to discharge after emergent colectomy at 3012 hospitals, 46.7% were discharged to a nonhome destination. There was a wide variation in risk and reliability-adjusted nonhome discharge rates across hospitals (15% to 80%). Patients at hospitals in the highest quintile of nonhome discharge rates were more likely to have longer hospitalizations (15.1 vs 13.2; P < 0.001) and more complications (43.2% vs 34%; P < 0.001). On multivariable analysis, only hospital ownership of a skilled nursing facility (P < 0.001), teaching status (P ¼ 0.025), and low nurse-to-patient ratios (P ¼ 0.002) were associated with nonhome discharges. Conclusions: Nearly half of Medicare beneficiaries are discharged to a nonhome destination after emergent colectomy. Hospital ownership of a skilled nursing facility and low nurse-to-patient ratios are highly associated with nonhome discharges. This may signify the underlying financial incentives to preferentially utilize postacute care facilities under the traditional feefor-service payment model. Keywords: colectomy, emergency, Medicare, outcome, postacute care, surgery
(Ann Surg 2015;xx:xxx–xxx)
A
pproximately 115,000 older adults undergo a colectomy every year,1 and about one-third do so for a life-threatening condition. After surviving their emergent operation, many patients experience significant postoperative functional decline, which limits their ability to return home. Ideally, patients are discharged home
From the Center for Healthcare Outcomes and Policy, Department of Surgery, University of Michigan, Ann Arbor, MI; yDepartment of Surgery, Mayo Clinic, Rochester, MN; and zDepartment of Surgery, University of Illinois Chicago, Chicago, IL. Funding statement: Z.M.A. is supported by AHRQ T32 HS000053-23. S.L.W. is supported by AHRQ 1K08 HS20937-01 and American Cancer Society-RSG12-269-01-CPHPS. Disclosures: The authors have no disclosures to make. Reprints: Sandra L. Wong, MD, MS, FACS, University of Michigan, 1500 East Medical Center Drive/3310 Cancer Center, SPC 5932, Ann Arbor, MI 481095932. E-mail: [email protected]. Copyright ß 2015 Wolters Kluwer Health, Inc. All rights reserved. ISSN: 0003-4932/14/26105-0821 DOI: 10.1097/SLA.0000000000001498
Annals of Surgery Volume XX, Number X, Month 2015
postoperatively, with or without home health services. A significant number of patients are discharged to nonhome destinations (rehabilitation centers, short-term recovery hospitals, skilled nursing facilities, swing bed units, or other facilities) to regain functional independence. As a proposed patient-centered outcome,2 the discharge destination may also be a marker for the quality of postoperative recovery.3,4 Although recuperating in a postacute care facility may be essential for optimal recovery, utilizing such facilities is a significant source of variation in Medicare payments in the United States,5,6 and it amounts to over $62 billion/year of health care dollars.6 Under the traditional fee-for-service reimbursement model, Medicare pays hospitals and postacute care providers separately, without regard to the quality or efficiency through the episode of care. Patients’ discharge plans were therefore often made for financial rather than clinical indications,7,8 and may be more common if the hospitals and postacute care facilities operate under the same financial organization. To this end, Medicare has proposed several bundled-payment and shared-savings programs to provide financial incentives to better integrate acute and postacute care, and reduce inappropriate utilization. Although the variations in other short-term outcomes such as morbidity, failure to rescue, and mortality after surgery have gained significant attention,9–11 limited data exist to help in understanding the variation in discharge destinations. Recently, Sacks et al12 identified several patient characteristics, such as older age, poor functional status, and the occurrence of postoperative complications, as risk factors for nonhome discharges. At the hospital level, Balentine et al3 demonstrated that high-volume hospitals are more likely to discharge patients home after colorectal surgery compared to low-volume hospitals. However, these studies do not examine several important hospital characteristics or financial relationships and infrastructure that may directly influence the discharge destination after surgery. In this context, we used national data on Medicare beneficiaries undergoing emergent colectomy to investigate the extent to which nonhome discharge rates vary by hospital after accounting for patient factors. Then, we sought to better understand the hospital characteristics that may explain this variation in discharge destinations. Specifically, we hypothesized that hospitals with existing partnerships with skilled nursing facilities would be more likely to discharge patients to such facilities. Ultimately, the findings of this study may be relevant to stakeholders and payers as they aim to integrate acute and postacute care.
METHODS Data Source and Study Population We used a national data set from the 100% Medicare Provider Analysis and Review (MedPAR) files for the years 2008, 2009, and 2010. The Centers for Medicare and Medicaid Services (CMS) maintains this database using claims submitted for all fee-for-service www.annalsofsurgery.com | 1
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
ANNSURG-D-15-01099; Total nos of Pages: 6;
ANNSURG-D-15-01099
Abdelsattar et al
acute care hospitalizations of Medicare beneficiaries not enrolled in managed care plans. We identified patients between the ages of 65 and 99 years who underwent a colon resection, using International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes (17.32, 17.33, 17.34, 17.35, 17.36, 45.73, 45.74, 45.75, 45.76, 45.80, 45.81, 45.82, 45.83). We excluded patients who did not present to the hospital from home, those who had their operation in 2007, and those who were discharged from the hospital in December 2010. As the discharge destination was the main outcome in this study, we excluded patients not surviving to discharge. The type and priority of the beneficiary’s admission to a facility for the inpatient hospital stay based on MedPAR codes were used to exclude patients who underwent elective operations.
Patient and Hospital Characteristics We evaluated patient characteristics including age, sex, race, and coexisting conditions. Coexisting conditions were identified by the appropriate ICD-9-CM diagnosis codes and were defined using the Elixhauser method.13 The type of colon resection (segmental or total) and operative approach (laparoscopic or open) were defined using appropriate ICD-9-CM procedure codes. Postoperative complications were identified using specific previously validated ICD-9CM diagnosis codes.14 We used the 2010 American Hospital Association (AHA) Annual Survey Database to determine specific hospital characteristics shown to be associated with improved postsurgical outcomes, namely, teaching status, hospital bed size, average daily census, and nurse-to-patient ratios. Nurse-to-patient ratios (or number of nurses per 1000 patient-days) were calculated by a previously published and widely used definition of evaluating productive nursing hours described by Spetz et al,15 and is calculated from AHA data by the following formula: [Nursing full-time equivalents (FTEs) 1768]/adjusted patient-days. We also specifically obtained data on the hospital’s financial relationships with skilled nursing facilities from the AHA files. Hospitals with incomplete AHA survey data were excluded, and only short-term acute care hospitals were included in this study.
Statistical Analysis The primary outcome of this study was the hospital risk and reliability-adjusted nonhome discharge rate. We calculated point estimates for this outcome as follows. First, we used standard logistic regression to calculate an empirically derived linear risk score for nonhome discharges. This score was calculated through the use of a continuous variable for age, dichotomous variables for sex and nonwhite race, and indicator variables for preexisting Elixhauser comorbidities, the occurrence of any complication, the operation performed, and the surgical approach (open vs laparoscopic). Second, we used a hierarchical mixed-effects logistic regression model that was based on the patient risk score (described above) with the hospital identifier as the random effect. Hierarchical regression is an advanced statistical technique that leverages empirical Bayes theorem to directly model variation at the hospital level. As a result, hierarchical modeling yields stable estimates of both coefficients and standard errors, reduces statistical noise, and provides more robust estimates of hospital quality. Finally, we ranked the hospitals according to their risk and reliability-adjusted nonhome discharge rates, and stratified the hospitals into quintiles of nonhome discharge rates. To identify the association between the structural hospital characteristics and the adjusted nonhome discharge rates, we used a generalized linear model with the logit link function, which allows calculating the absolute contribution of each characteristic to the adjusted odds of nonhome discharges over the average rate. 2 | www.annalsofsurgery.com
Annals of Surgery Volume XX, Number X, Month 2015
The univariate comparisons of the risk and reliability-adjusted nonhome discharge rates were made between the 2 quintiles at the extremes of nonhome discharges (ie, the top and bottom 20% of hospitals). When adjusting for reliability, hospitals with the lowest caseloads were shrunk toward the mean, thereby filling the middle quintiles.16 Therefore, focusing on the 2 quintiles at the extremes compares the hospitals that contribute the most information. All statistical tests were two-tailed, and a P value 2 Preoperative comorbidities Cardiovascular Congestive heart failure Hypertension Peripheral vascular disease Valvular disease Pulmonary Pulmonary circulation disease Chronic pulmonary disease Neurological Paralysis Other neurological disorders Endocrine/metabolic Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Obesity Weight loss Gastrointestinal Liver disease Peptic ulcer disease Renal Renal failure Fluid and electrolyte disorders Hematologic Acquired immune deficiency syndrome Lymphoma Metastatic cancer Solid tumor without metastasis Rheumatoid arthritis/collagen diseases Coagulopathy Chronic blood loss anemia Deficiency anemia Other Alcohol abuse Depression Drug abuse Psychoses
Home (n ¼ 65,399) 75.2 þ 7 28495 (43.6) 55240 (84.5)
Nonhome (n ¼ 57,205) 79.7 þ 7.7 20640 (36.1) 49348 (86.3)
29904 (45.7) 27651 (42.3) 7844 (12) 9734 (14.9) 16921 (25.9) 24064 (36.8)
21352 (37.3) 26227 (45.8) 9626 (16.8) 3831 (6.7) 31179 (54.5) 20208 (35.3)
5408 (8.3) 33573 (51.3) 3251 (5) 2264 (3.5)
P
