BACKGROUND: The Centers for Medicare & Medicaid Services (CMS) and Veterans Health Administration (VA) will report 30-day stroke readmission rates as a measure of hospital quality. A national debate on whether social risk factors should be included in models developed for hospital profiling is ongoing. OBJECTIVE: To compare a CMS-based model of 30-day readmission with a more comprehensive model that includes measures of social risk (such as homelessness) or clinical factors (such as stroke severity and functional status). DESIGN: Data from a retrospective cohort study were used to develop a CMS-based 30-day readmission model that included age and comorbid conditions based on codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (model 1). This model was then compared with one that included administrative social risk factors (model 2). Finally, the CMS model (model 1) was compared with a model that included social risk and clinical factors from chart review (model 3). These 3 models were used to rank hospitals by 30-day risk-standardized readmission rates and examine facility rankings among the models. SETTING: Hospitals in the VA. PARTICIPANTS: Patients hospitalized with stroke in 2007. MEASUREMENTS: 30-day readmission rates. RESULTS: The 30-day readmission rate was 12.8%. The c-statistics for the 3 models were 0.636, 0.646, and 0.661, respectively. All hospitals were classified as performing "as expected" using all 3 models (that is, performance did not differ from the VA national average); therefore, the addition of detailed clinical information or social risk factors did not alter assessment of facility performance. LIMITATION: A predominantly male veteran cohort limits the generalizability of these findings. CONCLUSION: In the VA, more comprehensive models that included social risk and clinical factors did not affect hospital comparisons based on 30-day readmission rates. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.
BACKGROUND: The Centers for Medicare & Medicaid Services (CMS) and Veterans Health Administration (VA) will report 30-day stroke readmission rates as a measure of hospital quality. A national debate on whether social risk factors should be included in models developed for hospital profiling is ongoing. OBJECTIVE: To compare a CMS-based model of 30-day readmission with a more comprehensive model that includes measures of social risk (such as homelessness) or clinical factors (such as stroke severity and functional status). DESIGN: Data from a retrospective cohort study were used to develop a CMS-based 30-day readmission model that included age and comorbid conditions based on codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (model 1). This model was then compared with one that included administrative social risk factors (model 2). Finally, the CMS model (model 1) was compared with a model that included social risk and clinical factors from chart review (model 3). These 3 models were used to rank hospitals by 30-day risk-standardized readmission rates and examine facility rankings among the models. SETTING: Hospitals in the VA. PARTICIPANTS: Patients hospitalized with stroke in 2007. MEASUREMENTS: 30-day readmission rates. RESULTS: The 30-day readmission rate was 12.8%. The c-statistics for the 3 models were 0.636, 0.646, and 0.661, respectively. All hospitals were classified as performing "as expected" using all 3 models (that is, performance did not differ from the VA national average); therefore, the addition of detailed clinical information or social risk factors did not alter assessment of facility performance. LIMITATION: A predominantly male veteran cohort limits the generalizability of these findings. CONCLUSION: In the VA, more comprehensive models that included social risk and clinical factors did not affect hospital comparisons based on 30-day readmission rates. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.
Authors: Oanh Kieu Nguyen; Anil N Makam; Christopher Clark; Song Zhang; Bin Xie; Ferdinand Velasco; Ruben Amarasingham; Ethan A Halm Journal: J Hosp Med Date: 2016-02-29 Impact factor: 2.960
Authors: Nicole Rosendale; Elan L Guterman; John P Betjemann; S Andrew Josephson; Vanja C Douglas Journal: Neurology Date: 2019-05-24 Impact factor: 9.910
Authors: Shayla N M Durfey; Amy J H Kind; Roee Gutman; Kristina Monteiro; William R Buckingham; Eva H DuGoff; Amal N Trivedi Journal: Health Aff (Millwood) Date: 2018-07 Impact factor: 6.301
Authors: Susannah M Bernheim; Craig S Parzynski; Leora Horwitz; Zhenqiu Lin; Michael J Araas; Joseph S Ross; Elizabeth E Drye; Lisa G Suter; Sharon-Lise T Normand; Harlan M Krumholz Journal: Health Aff (Millwood) Date: 2016-08-01 Impact factor: 6.301
Authors: Federico M Ghirimoldi; Susanne Schmidt; Richard C Simon; Chen-Pin Wang; Zhu Wang; Bradley B Brimhall; Paul Damien; Eric E Moffett; Laura S Manuel; Zaheer U Sarwar; Paula K Shireman Journal: J Gastrointest Surg Date: 2020-09-08 Impact factor: 3.452