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Benchmarking Danish hospitals on mortality and readmission rates after cardiovascular admission
Greg Ridgeway1,2,3, Mette Nørgaard4, Thomas Bøjer Rasmussen4
1Department of Criminology, University of Pennsylvania, Philadelphia, PA, USA, gridge@upenn.edu.
Insights
This study introduces a new benchmarking approach for hospital performance, which more accurately identifies outliers by considering patient case mix. This method reduces false positives compared to traditional regression techniques.
Area of Science:
- Health Services Research
- Health Outcomes Research
- Medical Informatics
Background:
- Assessing hospital performance is crucial for quality improvement.
- Existing performance measures may not adequately account for variations in patient populations (case mix).
- This can lead to inaccurate identification of outlier hospitals.
Purpose of the Study:
- To develop and evaluate a novel benchmarking approach for hospital performance measurement.
- To assess hospital performance by comprehensively accounting for patient case mix.
- To compare the proposed method with conventional regression techniques in identifying outlier hospitals.
Main Methods:
- A nationwide cohort registry-based study involving 331,513 cardiovascular patients in Denmark (2011-2015).
- Utilized propensity score weighting and doubly robust regression to create patient-matched benchmarks.
- Measured 30-day post-admission mortality and 30-day post-discharge readmission rates.
Main Results:
- The benchmarking approach identified fewer outlier hospitals compared to conventional regression methods.
- Five hospitals exceeded their benchmark for 30-day mortality (Number Needed to Harm: 55-137).
- Seven hospitals exceeded their benchmark for readmission (Number Needed to Harm: 22-71).
Conclusions:
- The proposed benchmarking approach provides a more thorough adjustment for patient case mix.
- This method reduces the risk of false-positive identification of outlier hospitals.
- A more comprehensive hospital performance measurement system could be based on this approach.
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.
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