Context Aware Recommendation Systems for Healthcare Service Personalization Based on Customer Journey Data

Abstract

Healthcare delivery increasingly spans digital front doors, physical encounters, and post-visit follow-up, creating fragmented experiences that patients perceive as a single journey. Personalization of healthcare services therefore benefits from recommender systems that account for context, temporal dependencies, and operational constraints rather than relying on static preference profiles. This paper examines context-aware recommendation for healthcare service personalization using customer journey data, where journeys are represented as multi-channel event streams covering search, scheduling, navigation, clinical touchpoints, and longitudinal engagement signals. The central technical challenge is to infer actionable intent and constraints from partially observed, irregularly sampled sequences while preserving safety, privacy, and equity. We develop a modeling view that unifies sequential representation learning, probabilistic state inference, and decision-focused optimization. Journey data are formalized as time-stamped heterogeneous events with latent health-service needs, and context is decomposed into patient state, situational factors, provider capacity, and policy constraints. We discuss learning objectives that combine ranking accuracy with calibrated uncertainty, and we analyze how counterfactual evaluation and constrained optimization can reduce exposure to harmful or inappropriate recommendations. Practical deployment considerations are addressed, including data minimization, privacy-preserving training, monitoring for distribution shift, and governance workflows that support clinician oversight. The result is a technical blueprint for building recommender systems that are sensitive to evolving context and the end-to-end customer journey, while remaining compatible with healthcare-specific requirements.