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

Area of Science:

• Health Services Research
• Behavioral Science
• Precision Health

Background:

• The increasing number of health consumers and patient-generated health data (PGHD) highlight the patient's central role in precision health and behavioral science.
• PGHD is crucial for health services but often overlooked, necessitating a framework for its systematic interpretation.

Purpose of the Study:

• To outline an interpretability-aware framework for PGHD to enhance its reliability and application in healthcare.
• To address the need for trustworthy insights from PGHD for population health management and personalized adaptive interventions.

Main Methods:

• This positional paper proposes a framework focusing on model properties and techniques that influence interpretability within patient-centered care.
• The framework aims to systematically process PGHD to ensure the trustworthiness of derived insights.

Main Results:

• The proposed framework improves the reliability of evidence generated from PGHD.
• It facilitates the development of adaptive interventions tailored to individual patient needs.
• Enhanced interpretability of PGHD leads to more trustworthy insights for applications like person-centered comparative effectiveness.

Conclusions:

• An interpretability-aware framework is essential for systematically making sense of PGHD.
• This approach enhances the trustworthiness of PGHD-derived insights and their application in patient-centered care.
• The framework supports both population health management and individualized adaptive interventions.