An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure

Keenan, Patricia S.; Normand, Sharon‐Lise T.; Lin, Zhenqiu; Drye, Elizabeth E.; Bhat, Kanchana; Ross, Joseph S.; Schuur, Jeremiah D.; Stauffer, Brett D.; Bernheim, Susannah M.; Epstein, Andrew J.; Wang, Yongfei; Herrin, Jeph; Chen, Jersey; Federer, Jessica; Mattera, Jennifer A.; Wang, Yun; Krumholz, Harlan M.

doi:10.1161/circoutcomes.108.802686

Cited by 465 publications

(534 citation statements)

References 26 publications

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“…[3][4][5][6] At present, the current CMS readmission and mortality models for CAP and HF do not adjust for any Level 2 and 3 social factors identified in this review. Future research should attempt to take into account more of these other social factors that may affect adverse outcomes, but are not within the providers control and are independent of the quality of inpatient care and discharge coordination.…”

Section: Commentsmentioning

confidence: 99%

“…However, most risk models designed to predict readmission and mortality do not include social factors. 2 The models developed by Krumholz et al [3][4][5][6] that are used by the Centers for Medicaid and Medicare (CMS) to profile hospitals control for disease severity, comorbidity, age and gender. According to Andersen's behavioral model, 7 many different aspects of a patient's social, behavioral, and environmental milieu could likely influence post-discharge outcomes through several different mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review

et al. 2012

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Section: Commentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review

et al. 2012

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“…Prior CMS models for hospital readmission risk considered only medical comorbidities and basic demographic factors, [63][64][65] with a relatively poor ability to predict hospital readmissions (c-statistics 0.60-0.63). The addition of functional measures can improve current CMS readmission risk models to more accurately identify patients at high risk of readmission and more fairly reimburse hospitals based on performance.…”

Section: Discussionmentioning

confidence: 99%

Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

Shih

Gerrard²,

Goldstein

et al. 2015

J GEN INTERN MED

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OBJECTIVE:To examine functional status versus medical comorbidities as predictors of acute care readmissions in medically complex patients. DESIGN: Retrospective database study. SETTING: U.S. inpatient rehabilitation facilities. PARTICIPANTS: Subjects included 120,957 patients in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011. INTERVENTIONS: A Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Functional status was measured by the FIM ® motor score. The Basic Model was compared to six other predictive models-three Basic Plus Models that added a comorbidity measure to the Basic Model and three GenderComorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance. MAIN OUTCOME MEASURES: We investigated 3-, 7-, and 30-day readmission to acute care hospitals from inpatient rehabilitation facilities. RESULTS: Basic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively. The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model, and the best-performing GenderComorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model. CONCLUSIONS:Readmission models based on functional status consistently outperform models based on medical comorbidities. There is opportunity to improve current national readmission risk models to more accurately predict readmissions by incorporating functional data.

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“…The use of administrative data has been shown to be valid for comparing death rates across hospitals, 3 and methods have been developed to better analyze administrative data for profiling hospital performance for the treatment of patients with heart failure and acute myocardial infarction. 6,8 However, there is concern about the relevance of such data because their use was originally developed for billing purposes. 4 Prior studies have also highlighted the limitations of using administrative claims data, especially for determining detailed information.…”

Section: Discussionmentioning

confidence: 99%

Pitfalls of calculating hospital readmission rates based on nonvalidated administrative data sets

Amin¹,

Schairer

et al. 2013

SPI

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Object Administrative databases are increasingly being used to establish benchmarks for quality of care and to compare performance across peer hospitals. As proposals for accountable care organizations are being developed, readmission rates will be increasingly scrutinized. The purpose of the present study was to assess whether the all-cause readmissions rate appropriately reflects the University of California, San Francisco (UCSF) Medical Center hospital's clinically relevant readmission rate for spine surgery patients and to identify predictors of readmission. Methods Data for 5780 consecutive patient encounters managed by 10 spine surgeons at UCSF Medical Center from October 2007 to June 2011 were abstracted from the University HealthSystem Consortium (UHC) using the Clinical Data Base/Resource Manager. Of these 5780 patient encounters, 281 patients (4.9%) were rehospitalized within 30 days of the previous discharge date. The authors performed an independent chart review to determine clinically relevant reasons for readmission and extracted hospital administrative data to calculate direct costs. Univariate logistic regression analysis was used to evaluate possible predictors of readmission. The two-sample t-test was used to examine the difference in direct cost between readmission and nonreadmission cases. Results The main reasons for readmission were infection (39.8%), nonoperative management (13.4%), and planned staged surgery (12.4%). The current all-cause readmission algorithm resulted in an artificially high readmission rate from the clinician's point of view. Based on the authors' manual chart review, 69 cases (25% of the 281 total readmissions) should be excluded because 39 cases (13.9%) were planned staged procedures; 16 cases (5.7%) were unrelated to spine surgery; and 14 surgical cases (5.0%) were cancelled or rescheduled at index admission due to unpredictable reasons. When these 69 cases are excluded, the direct cost of readmission is reduced by 29%. The cost variance is in excess of $3 million. Predictors of readmission were admission status (p < 0.0001), length of stay (p = 0.0001), risk of death (p < 0.0001), and age (p = 0.021). Conclusions The authors' findings identify the potential pitfalls in the calculation of readmission rates from administrative data sets. Benchmarking algorithms for defining hospitals' readmission rates must take into account planned staged surgery and eliminate unrelated reasons for readmission. When this is implemented in the calculation method, the readmission rate will be more accurate. Current tools overestimate the clinically relevant readmission rate and cost.

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An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure

Cited by 465 publications

References 26 publications

Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review

Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review

Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

Pitfalls of calculating hospital readmission rates based on nonvalidated administrative data sets

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