Integrating Artificial Intelligence, Electronic Health Records, and Wearables for Predictive, Patient-Centered...

Area of Science:

• Digital Health
• Health Informatics
• Human-Computer Interaction

Background:

• Fragmented digital health solutions often fail to meet user expectations.
• Integrating artificial intelligence (AI), wearable data, electronic health records (EHRs), and patient-reported outcomes (PROs) can personalize healthcare.
• Current systems lack alignment with patient and stakeholder needs for proactive, integrated care.

Purpose of the Study:

• Explore patient and stakeholder needs for AI-driven integrated digital health systems.
• Identify key priorities for designing user-centered digital health platforms.
• Propose a conceptual framework to guide future development and validation.

Main Methods:

• Conducted semi-structured interviews with 44 participants (Health Enthusiasts, Chronic Condition Managers, Low-Engagement Users) using the NSF Innovation Corps (I-Corps) framework.
• Employed thematic analysis with a hybrid deductive-inductive approach.
• Explored user expectations through qualitative customer discovery.

Main Results:

• Identified four key priorities: data interoperability, actionable personalization, AI trust and transparency (with clinician oversight), and usability.
• Found age- and persona-specific differences: younger users preferred predictive features, while older users prioritized safety and clinical integration.
• Highlighted the need for unified data from wearables, EHRs, and self-reports for effective digital health.

Conclusions:

• Stakeholder needs identified in this study informed a conceptual framework for integrated digital health platforms.
• The framework serves as a blueprint for developing patient- and provider-centered systems.
• Preliminary findings emphasize the importance of user-centered design in digital health innovation.