Journal Article

Healthcare trajectories of aging individuals during their last year of life: application of process mining methods to administrative health databases

Langford-Avelar, Alexandra, Duc, Juliette, Bosson-Rieutort, Delphine, Dalmas, Benjamin

2025

Overview

Context World is aging and the prevalence of chronic diseases is raising with age, increasing financial strain on organizations but also affecting patients’ quality of life until death. Research on healthcare trajectories has gained importance, as it can help anticipate patients’ needs and optimize service organization. In an overburdened system, it is essential to develop automated methods based on comprehensive and reliable and already available data to model and predict healthcare trajectories and future utilization. Process mining, a family of process management and data science techniques used to derive insights from the data generated by a process, can be a solid candidate to provide a useful tool to support decision-making. Objective We aimed to (1) identify the healthcare baseline trajectories during the last year of life, (2) identify the differences in trajectories according to medical condition, and (3) identify adequate settings to provide a useful output. Methods We applied process mining techniques on a retrospective longitudinal cohort of 21,255 individuals who died between April 1, 2014, and March 31, 2018, and were at least 66 years or older at death. We used 6 different administrative health databases (emergency visit, hospitalisation, homecare, medical consultation, death register and administrative), to model individuals’ healthcare trajectories during their last year of life. Results Three main trajectories of healthcare utilization were highlighted: (i) mainly accommodating a long-term care center; (ii) services provided by local community centers in combination with a high proportion of medical consultations and acute care (emergency and hospital); and (iii) combination of consultations, emergency visits and hospitalization with no other management by local community centers or LTCs. Stratifying according to the cause of death highlighted that LTC accommodation was preponderant for individuals who died of physical and cognitive frailty. Conversely, services offered by local community centers were more prevalent among individuals who died of a terminal illness. This difference is potentially related to the access to and use of palliative care at the end-of-life, especially home palliative care implementation. Conclusion Despite some limitations related to data and visual limitations, process mining seems to be a method that is both relevant and simple to implement. It provides a visual representation of the processes recorded in various health system databases and allows for the visualization of the different trajectories of healthcare utilization.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Administrative health data / Aged / Aged, 80 and over / Aging / Analysis / Automation / Chronic illnesses / Community centers / Data mining / Data Mining - methods / Data science / Databases, Factual - statistics & numerical data / Death / Decision making / Disease / Emergency management / Emergency medical care / End-of-life / Event log / Female / Health aspects / Health care / Health Informatics / Healthcare utilization / Humans / Information Systems and Communication Service / Long-term care / Longitudinal Studies / Machine learning / Male / Management of Computing and Information Systems / Medical care / Medicine / Medicine & Public Health / Mortality / Palliation / Pareto optimum / Patients / Population / Process management / Process mining / Quality of life / Retrospective Studies / Terminal Care - statistics & numerical data / Terminal diseases / Trajectories / Utilization