Evidence map›Paper›PMID 39899851›Full record

ArticleJMIR human factors2025

Patient-Generated Collections for Organizing Electronic Health Record Data to Elevate Personal Meaning, Improve Actionability, and Support Patient-Health Care Provider Communication: Think-Aloud Evaluation Study.

Drashko Nakikj, David Kreda, Karan Luthria, Nils Gehlenborg

Abstract readEvaluation Study
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Drashko NakikjDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-9723-9138
David KredaDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-7132-8220
Karan LuthriaDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-4392-9675
Nils GehlenborgDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0003-0327-8297

Funding

Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7M
NIH HHS U24 OD023176
6 · The paper itself

Abstract

backgroundThrough third party applications, patients in the United States have access to their electronic health record (EHR) data from multiple health care providers. However, these applications offer only a predefined organization of these records by type, time stamp, or provider, leaving out meaningful connections between them. This prevents patients from efficiently reviewing, exploring, and making sense of their EHR data based on current or ongoing health issues. The lack of personalized organization and important connections can limit patients' ability to use their data and make informed health decisions.

objectiveTo address these challenges, we created Discovery, an experimental app that enables patients to organize their medical records into collections, analogous to placing pictures in photo albums. These collections are based on the evolving understanding of the patients' past and ongoing health issues. The app also allows patients to add text notes to collections and their constituent records. By observing how patients used features to select records and assemble them into collections, our goal was to learn about their preferred mechanisms to complete these tasks and the challenges they would face in the wild. We also intended to become more informed about the various ways in which patients could and would like to use collections.

methodsWe 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 within them, and retaining insights, as well as exploring use cases. We conducted reflexive thematic analysis on the transcribed feedback.

resultsCollections were useful for personal use (quick access to information, reflection on medical history, tracking health, journaling, and learning from past experiences) and clinical visits (preparation and raising physicians' awareness). Assembling EHR data into reliable collections could be difficult for typical patients due to considerable manual work and lack of medical knowledge. However, automated collection building could alleviate this issue. Furthermore, having EHR data organized in collections may have limited use. However, augmenting them with patient-generated data, which are entered with flexible richness and structure, could add context, elevate meaning, and improve actionability. Finally, collections might produce a misconstrued health picture, but bringing the physician in the loop for verification could increase their clinical validity.

conclusionsCollections can be a powerful tool for advancing patients' proactivity, awareness, and self-advocacy, potentially facilitating patient-centered care. However, patients need better support for incorporating their own everyday data and adding meaningful annotations for future reference. Improvements in the comprehensiveness, efficiency, and reliability of the collection assembly process through automation are also necessary.

Indexed as

Electronic Health RecordsMobile ApplicationsPatient ParticipationAdultAmbulatory CareFemaleHealth CommunicationHealth Records, PersonalHealth StatusHumansMaleMiddle Agedawarenesscollectionsdata organizationelectronic health recordsmobile healthpatient–health care provider communicationpatientsproactivityself-advocacysensemaking

Identifiers

PMID39899851
PMCPMC11833264

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.