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Cited by 42Mobile Health and mHealth ApplicationsInnovation in Digital Healthcare SystemsElectronic Health Records Systems

Using a personal health library-enabled mHealth recommender system for self-management of diabetes among underserved populations: use case for knowledge graphs and linked data

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Ammar, Nariman, Bailey, James E, Davis, Robert L, Shaban-Nejad, Arash·2021·JMIR Formative Research
DOI 10.2196/24738PMC PMC8075073

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Abstract

Background: Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients’ health by understanding medical events in the context of their lives. Objective: This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients’ PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. Methods: We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. Results: To showcase the main features of the mobile health app and the PHL, we mapped those features onto a framework comprising the user requirements identified in a use case scenario that features a preventive intervention from the diabetes self-management domain. Ongoing development of the app requires a formative evaluation study and a clinical trial to assess the impact of the digital intervention on patient-users. We provide synopses of both study protocols. Conclusions: The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge. By exposing the PHL functionality as an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.

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  • AI has become increasingly embedded in medical practice over the past decade, enhancing care capabilities from personalized health libraries and patient education to disease screening, diagnostics, and treatment planning.

    “Artificial intelligence (AI) has become increasingly embedded in medical practice over the past decade, enhancing care capabilities from the development of personalized health libraries and innovative patient education strategies to disease screening, diagnostics, and treatment planning [1-4].”

    From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [3]

    The Introduction (Overview; page unavailable) describes personal health libraries that can generate intelligent personalized interventions, and the Discussion (page unavailable) proposes AI-based personalized resource suggestions. The Review of Relevant Literature also mentions a tool for cancer staging and identifying treatment options. The paper does not establish a decade-long increase in AI use across medical practice or support the full range of screening and diagnostic capabilities attributed to the citation group. (LLM, conf=0.83)

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Cited by

Sorted by citations

Authors
Ammar, N., Bailey, J. E., Davis, R. L., & Shaban-Nejad, A.
Affiliations
  • Oak Ridge National Laboratory (US)
  • University of Tennessee Health Science Center (US)
ORCID
Venue
JMIR Formative Research
Publisher
JMIR Publications Inc.
Published
2021-03-16
Volume
5
Issue
3
Pages
e24738
ISSN
2561-326X (ISSN-L)
DOI
10.2196/24738
PMC
PMC8075073
OpenAlex
W3133264142
Cited by
42
References
2
Citations by year
2026: 5 · 2025: 5 · 2024: 9 · 2023: 11 · 2022: 10 · 2021: 2
FWCI
8.99
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Formative Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
diabetes self-management, underserved populations, linked data, knowledge graphs, mobile health apps, electronic health records, interoperability, preventive interventions

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