Typical Healthcare Pathways as a Basis for Admixture Modeling of Patient Trajectories

2026-06-12

AI Summary

This study addresses the heterogeneity in patients’ clinical trajectories—particularly in diagnostic and therapeutic sequences and their temporal documentation—by proposing a two-stage interpretable modeling framework. First, a rule-based algorithm extracts representative care pathways from cohort data; these pathways are then formalized as Markov chains and integrated as mixture components into a weighted probabilistic model of individual patient trajectories. This approach innovatively decouples population-level pathway discovery from patient-specific modeling, enabling each patient’s trajectory to be represented as a probabilistic combination of multiple canonical pathways. The method enhances both interpretability and clinical utility, as demonstrated on real-world data from radical prostatectomy patients, where it consistently identified clinically meaningful care patterns and effectively facilitated patient subgroup discovery.

Abstract

Background: Understanding whether patients follow similar or distinct patterns of care is important for characterizing clinical practice, identifying patient subgroups, and supporting quality improvement. However, routine healthcare trajectories are difficult to compare directly because patients may differ in their diagnostic workup, treatment sequencing, timing of clinical events, and documentation practices. Despite this variation, trajectories often contain recurring patterns at the cohort level. Methods: To address this challenge, we present a framework that explicitly separates cohort-level typical pathway identification from patient-level inference. At the cohort level, we derive an interpretable representation of care processes using a rule-based algorithm to identify typical healthcare pathways, resulting in a compact pathway graph. These pathways are then modeled as Markov chains and used as structured components in an admixture model, allowing each patient to be represented as a probabilistic mixture of typical pathways rather than being assigned to a single pathway component. The resulting admixture weights provide a compact representation of patient trajectories for subgroup characterization. We further assess the stability of the identified pathways and inferred admixture representations across multiple train-test splits. Results: Across train-test splits, the framework demonstrated consistent pathway structures and patient-level mixture patterns. Applied to routine care data from prostate cancer patients undergoing radical prostatectomy, the framework identified interpretable care patterns and supported the identification of patient subgroups with similar clinical event patterns. Conclusion: Overall, the proposed framework provides an interpretable and stable approach for summarizing treatment pathways and characterizing patient subgroups in real-world practice.

Problem

• healthcare pathways
• patient trajectories
• admixture modeling
• clinical practice patterns
• patient subgroups

Innovation

• admixture modeling
• typical healthcare pathways
• Markov chains
• patient trajectory representation
• interpretable pathway graph