A semi-Markov multi-state model identified distinct clusters of healthcare providers, including 5 latent populations for discharge, 3 for in-hospital death, and 4 for readmission.
Cohort (n=42,871)
Yes
A semi-Markov multi-state model successfully identified clusters of healthcare providers with extreme behavior patterns across different transitions in heart failure patient pathways, allowing for a comprehensive assessment of performance.
BACKGROUND: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. METHODS: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. RESULTS: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). CONCLUSIONS: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.
Gasperoni et al. (Fri,) conducted a cohort in Heart Failure (n=42,871). Healthcare provider clusters was evaluated on Transition times between health states. A semi-Markov multi-state model identified distinct clusters of healthcare providers, including 5 latent populations for discharge, 3 for in-hospital death, and 4 for readmission.
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