Making Sense of Patient-Generated Health Data for Interpretable Patient-Centered Care: The Transition from "More" to "Better"

Abstract

The rise of health consumers and the accumulation of patient-generated health data (PGHD) have brought the patient to the centerstage of precision health and behavioral science. In this positional paper we outline an interpretability-aware framework of PGHD, an important but often overlooked dimension in health services. The aim is two-fold: First, it helps generate practice-based evidence for population health management; second, it improves individual care with adaptive interventions. However, how do we check if the evidence generated from PGHD is reliable? Are the evidence directly deployable in realworld applications? How to adapt behavioral interventions for each individual patient at the touchpoint given individual patients' needs? These questions commonly require better interpretability of PGHD-derived patient insights. Yet the definitions of interpretability are often underspecified. In the position paper, we outline an interpretability-aware framework to handle model properties and techniques that affect interpretability in the patient-centered care process. Throughout the positional paper, we contend that making sense of PGHD systematically in such an interpretability-aware framework is preferrable, because it improves on the trustworthiness of PGHD-derived insights and the consequent applications such as person-centered comparative effectiveness in patient-centered care.

Keywords: Informatics; Machine Learning; Patient-Centered Care.