Benchmarking Danish hospitals on mortality and readmission rates after cardiovascular admission

Greg Ridgeway,1–3 Mette Nørgaard,4 Thomas Bøjer Rasmussen,4 William D Finkle,3 Lars Pedersen,4 Hans Erik Bøtker,5 Henrik Toft Sørensen4 1Department of Criminology, University of Pennsylvania, Philadelphia, PA, USA; 2Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA; 3Consolidated Research, Inc., Los Angeles, CA, USA; 4Department of Clinical Epidemiology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark; 5Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark Objective: The aim of this study was to examine hospital performance measures that account more comprehensively for unique mixes of patients’ characteristics. Design: Nationwide cohort registry-based study within a population-based health care system. Participants: In this study, 331,513 patients discharged with a primary cardiovascular diagnosis from 1 of 26 Danish hospitals during 2011–2015 were included. Data covering all Danish hospitals were drawn from the Danish National Patient Registry and the Danish National Health Service Prescription Database. Main outcome measures: Thirty-day post-admission mortality rates, 30-day post-discharge readmission rates, and the associated numbers needed to harm were measured. Methods: For each index hospital, we used a non-parametric logistic regression model to compute propensity scores. Propensity score weighted patients treated at other hospitals collectively resembled patients treated at the index hospital in terms of age, sex, primary discharge diagnosis, diagnosis history, medications, previous cardiac procedures, and comorbidities. Outcomes for the weighted patients treated at other hospitals formed benchmarks for the index hospital. Doubly robust regression formally tested whether the outcomes of patients at the index hospital differed from the outcomes of the patients used to form the benchmarks. For each index hospital, we computed the false discovery rate, ie, the probability of being incorrect if we claimed the hospital differed from its benchmark. Results: Five hospitals exceeded their benchmark for 30-day mortality rates, with the number needed to harm ranging between 55 and 137. Seven hospitals exceeded their benchmark for readmission, with the number needed to harm ranging from 22 to 71. Our benchmarking approach flagged fewer hospitals as outliers compared with conventional regression methods. Conclusion: Conventional methods flag more hospitals as outliers than our benchmarking approach. Our benchmarking approach accounts more thoroughly for differences in hospitals’ patient case mix, reducing the risk of false-positive selection of suspected outliers. A more comprehensive system of hospital performance measurement could be based on this approach. Keywords: performance measurement, propensity score, doubly robust estimation, case mix adjustment, cohort study