Healthcare trajectories of aging individuals during their last year of life: application of process mining methods to administrative health databases
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".