1. Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model
- Author
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Christopher Jackson, Francesca Gasperoni, Anna Maria Paganoni, Francesca Ieva, Linda D. Sharples, Gasperoni, Francesca [0000-0002-1713-9477], and Apollo - University of Cambridge Repository
- Subjects
Male ,Databases, Factual ,Health Personnel ,030204 cardiovascular system & hematology ,01 natural sciences ,Health informatics ,Patient Readmission ,Clustering ,Health administration ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Bayesian information criterion ,Health care ,Outcome Assessment, Health Care ,Medicine ,Humans ,Nonparametric frailty ,0101 mathematics ,Aged ,Aged, 80 and over ,Heart Failure ,Actuarial science ,Markov chain ,business.industry ,Health Policy ,Nursing research ,lcsh:Public aspects of medicine ,Nonparametric statistics ,Bayes Theorem ,lcsh:RA1-1270 ,Multi-state model ,Middle Aged ,Hospitals ,Patient Discharge ,Hospitalization ,Identification (information) ,Quality, performance, safety and outcomes ,Italy ,Critical Pathways ,Female ,business ,Decision making ,Research Article - Abstract
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.
- Published
- 2020