Towards Personalized Care Coordination Service: Integrating Guideline-Based Practice with Interactive Data-Driven Analytics

Transforming clinical requirements into personalized care plans is a challenging task. Existing care coordination services account for the most common physiology -based needs addressed in clinical guidelines, but do not discern different individuals' unique needs. Moreover, the individual difference-conferring signals are not explicitly recorded in patient health records and often can only be captured by integrating data sources obtained from a multitude of service providers. The quest for better personalized care coordination services leads to the exploration of two dimensions: what are the properties of tailoring matrix to personalize care coordination plans, and how to use the tailoring matrix to improve the relevance of care plans and provide individual feedbacks. In this paper, we present a personalization service framework that accounts for the two dimensions, leveraging the longitudinal records of other cohort patients whom have been dynamically identified as exhibiting patterns similar to the patient in focus. We summarize the design rationale and overall operation of the framework as well as details of the interactive analytics components as to how to capture individual differences and optimize expected utility of the coordinated care services in a continuous feedback loop.