ArticleBMC medical research methodology2019
Ranking hospital performance based on individual indicators: can we increase reliability by creating composite indicators?
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Methods to derive composite indicators used for quality and safety measurement and monitoring in healthcare: a scoping review.Frontiers in health services · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Operating Room Performance Optimization Metrics: a Systematic Review.Journal of medical systems · 2023Pooled it
- Hospital variation in surgical outcomes for gastric cancer: the impact of case-mix and treatment across a global cohort.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Dynamic evaluation system for improving hospital outsourcing service performance: a G1-Critic and LSTM+Dropout approach.BMC health services research · 2026Article
- Propensity score weighting analysis with complex survey data for estimating population-level treatment effects on survival: a simulation study.Health services & outcomes research methodology · 2025Article
- Article
- Article
- Statistical Analysis and Forecasts of Performance Indicators in the Romanian Healthcare System.Healthcare (Basel, Switzerland) · 2025Article
- Comprehensive Evaluation of Quality Indicators: Analyzing the Dutch Breast Cancer Audit.International journal of health policy and management · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- Observational
- The impact of surgical volume on hospital ranking using the standardized infection ratio.Scientific reports · 2023Article
- The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in Monte Carlo simulations.BMC medical research methodology · 2023Article
- Stability of hospital quality indicators over time: A multi-year observational study of German hospital data.PloS one · 2023Observational
- A more comprehensive evaluation of quality of care after total hip and knee arthroplasty: combining 4 indicators in an ordered composite outcome.Acta orthopaedica · 2022Review
- Composite measures of quality of health care: Evidence mapping of methodology and reporting.PloS one · 2022Article
- Comparison of cost and complication rates for profiling hospital performance in lumbar fusion for spondylolisthesis.The spine journal : official journal of the North American Spine Society · 2021Article
- Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
backgroundReport cards on the health care system increasingly report provider-specific performance on indicators that measure the quality of health care delivered. A natural reaction to the publishing of hospital-specific performance on a given indicator is to create 'league tables' that rank hospitals according to their performance. However, many indicators have been shown to have low to moderate rankability, meaning that they cannot be used to accurately rank hospitals. Our objective was to define conditions for improving the ability to rank hospitals by combining several binary indicators with low to moderate rankability.
methodsMonte Carlo simulations to examine the rankability of composite ordinal indicators created by pooling three binary indicators with low to moderate rankability. We considered scenarios in which the prevalences of the three binary indicators were 0.05, 0.10, and 0.25 and the within-hospital correlation between these indicators varied between - 0.25 and 0.90.
resultsCreation of an ordinal indicator with high rankability was possible when the three component binary indicators were strongly correlated with one another (the within-hospital correlation in indicators was at least 0.5). When the binary indicators were independent or weakly correlated with one another (the within-hospital correlation in indicators was less than 0.5), the rankability of the composite ordinal indicator was often less than at least one of its binary components. The rankability of the composite indicator was most affected by the rankability of the most prevalent indicator and the magnitude of the within-hospital correlation between the indicators.
conclusionsPooling highly-correlated binary indicators can result in a composite ordinal indicator with high rankability. Otherwise, the composite ordinal indicator may have lower rankability than some of its constituent components. It is recommended that binary indicators be combined to increase rankability only if they represent the same concept of quality of care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.