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Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model
- Source :
- BMC Health Services Research, Vol 20, Iss 1, Pp 1-11 (2020), BMC Health Services Research
- Publication Year :
- 2020
- Publisher :
- BioMed Central, 2020.
-
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.
- 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
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- BMC Health Services Research, Vol 20, Iss 1, Pp 1-11 (2020), BMC Health Services Research
- Accession number :
- edsair.doi.dedup.....f248e772db5dbccfedd5d9d257982d4c