Need Assessment of the Development of an AI-integrated Personal Health Record (PHR) System for Summarizing Patient Data to Enhance Clinical Decision-making

Abstract

Background: The exponential growth of healthcare data presents significant challenges for clinicians and patients alike. Personal Health Record (PHR) systems, enhanced with Artificial Intelligence (AI), offer the potential to automatically summarize complex patient data, thereby improving clinical decision-making and patient engagement. However, user readiness, adoption barriers, and specific feature needs remain underexplored, especially in low- and middle-income settings. Objective: This study aimed to assess the need, perceptions, and acceptance of an AI-integrated electronic PHR system designed to summarize patient data and enhance clinical workflows, from the perspectives of healthcare professionals and patients. Methods: A cross-sectional survey was conducted among 384 participants (195 healthcare workers and 188 patients) across a multi-specialty healthcare network in India. Validated questionnaires measured current health record management challenges, awareness of digital health initiatives like the Ayushman Bharat Health Account (ABHA), and preferences and concerns related to AI-enabled PHR adoption. Descriptive and inferential statistical analyses evaluated user readiness and feature prioritization. Findings: While smartphone ownership reached 100% among patients, traditional paper records remain prevalent (74%). Both patients and healthcare workers reported critical issues with data fragmentation, record loss, duplicate testing, and administrative burden. Awareness of ABHA was high among professionals (89%) but limited in patients (26%), with usage below 6% in both groups. Despite this, over 90% expressed a strong willingness to adopt AI-supported PHR solutions, emphasizing automated summarization, secure digital lockers, and mobile accessibility. Privacy, data accuracy, and training emerged as primary concerns. Interpretation: These findings reveal a pressing need and promising acceptance for AI-integrated PHR systems that address key pain points in health data management. To optimize adoption, future system development must prioritize user-centered design, robust privacy safeguards, explainable AI, and integration within national digital health frameworks.

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