Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model

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.

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure

“…By including absorbing states to the regular Markov chain, the model becomes an absorbing Markov chain (AMC). 23 , 24 , 25 , 26 The transition probability matrix (equation b ) for the absorbing Markov chain is an extended version of the regular chain:…”

“…Thus the probability of moving to any other state is , and the probability of remaining in the state is always 21,22 Non‐absorbing states are called transient states . By including absorbing states to the regular Markov chain, the model becomes an absorbing Markov chain (AMC) 23–26 . The transition probability matrix (equation b) for the absorbing Markov chain is an extended version of the regular chain:…”

Similarity of competing risks models with constant intensities in an application to clinical healthcare pathways involving prostate cancer surgery

“…Note that in case of right-censoring the methodology described above can be extended by adding corresponding factors from the distribution of the censoring times to the likelihood in (7). This requires the assumption of independence between censoring times and survival times.…”

“…Gasperoni et al 6 investigated multi‐state models for evaluating the impact of risk factors on heart failure care paths involving multiple hospital admissions, admissions to home care or intermediate care units or death. Gasperoni et al 7 considered potential similarities and differences among healthcare providers on the clinical path of heart failure patients.…”

Similarity of competing risks models with constant intensities in an application to clinical healthcare pathways involving prostate cancer surgery

“…Right-censoring. Note that in case of right-censoring the methodology described above can be extended by adding corresponding factors from the distribution of the censoring times to the likelihood in (7). This requires the assumption of independence between censoring times and survival times.…”