A conceptual framework for modeling longitudinal healthcare encounter data

摘要

We discuss a framework for analyzing data concerning healthcare encounters at the individual level. These encounters can be of various types - outpatient, emergency room, inpatient, pharmaceutical etc., each corresponding to one or more diagnoses. Each encounter happens on a certain day (or a certain hour) and when such data is collected over a period of time, it creates an evolving point process unique to each individual. The point process provides information about the intensity and diversity of encounters - how frequent and how fragmented the care is across multiple settings. When such longitudinal “point process” data is available for a cohort of individuals, it is possible to analyze the aggregate burden of managing the cohort's care in a particular time period. We provide examples where such data could be used and discuss the stochastic methods that are best suited for generating insights.