JMIR Publications

ABSTRACT

Background:

Through third party applications, patients in the US have access to their electronic health records (EHR) data from multiple providers. However, these applications lack personalized data organization as they provide limited ways to view the aggregated medical records - typically by type, timestamp, or provider, leaving out meaningful connections between them. This prevents patients from reviewing and making sense of their EHR data based on current or ongoing health issues. In addition, existing solutions do not allow patients to save their sensemaking work for later reference. Consequently, patients face situations where they could overlook important information, lose context or forget insights. These pitfalls may result in forming skewed impressions of their health, potentially contributing to decisions that could put patients at risk.

Objective:

To address these sensemaking challenges we introduced an experimental iPhone app, Discovery, that allows patients to organize their medical records in collections based on their health issues, and to add notes to those collections and individual records within them. By observing how patients engage EHR data using Discovery, our goal was to learn about their preferred mechanisms for creating collections and populating them with relevant records, along with identifying potential use cases.

Methods:

We conducted a think aloud evaluation study with 14 participants on synthetic data. In Session 1, we obtained feedback on the mechanics for creating and assembling collections, and adding notes. In Session 2, we focused on reviewing collections, finding data patterns with them, and retaining insights, as well as exploring use cases. We conducted reflexive thematic analysis on the transcribed feedback.

Results:

Collections are useful for personal use: quick access to information, reflection on medical history, tracking health, and learning from past experiences; and clinical visits: preparation, filling out forms, and raising physicians’ awareness. However, independent assembling of EHR data into reliable collections can be difficult for typical patients due to the substantial manual work and lack of medical knowledge, but automating this process could alleviate this issue. Further, having EHR data organized in collections may come with limited utilization, but augmenting them with patient generated data (PGD), which are entered in flexible ways to capture desired degree of richness and structure, could add context, elevate meaning and improve actionability. Finally, the collections might produce a misconstrued health picture, but bringing the physician in the loop as a supervisor through collaborative editing could increase their clinical validity.

Conclusions:

Collections can be a powerful tool for advancing patients’ proactivity, awareness and self-advocacy. However, more needs to be done to improve the efficiency and reliability of the collection assembly process using automatic approaches. Shared and editable collections may have the potential to promote partnership between the patients and the physicians for contextualization and continuity in problem solving, and safer clinical actions.