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Personal health libraries (PHLs) provide a single point of secure access topatients digital health data and enable the integration of knowledge stored intheir digital health profiles with other sources of global knowledge. PHLs canhelp empower caregivers and health care providers to make informed decisionsabout patients health by understanding medical events in the context of theirlives. This paper reports the implementation of a mobile health digitalintervention that incorporates both digital health data stored in patients PHLsand other sources of contextual knowledge to deliver tailored recommendationsfor improving self-care behaviors in diabetic adults. We conducted a thematicassessment of patient functional and nonfunctional requirements that aremissing from current EHRs based on evidence from the literature. We used theresults to identify the technologies needed to address those requirements. Wedescribe the technological infrastructures used to construct, manage, andintegrate the types of knowledge stored in the PHL. We leverage the SocialLinked Data (Solid) platform to design a fully decentralized and privacy-awareplatform that supports interoperability and care integration. We provided aninitial prototype design of a PHL and drafted a use case scenario that involvesfour actors to demonstrate how the proposed prototype can be used to addressuser requirements, including the construction and management of the PHL and itsutilization for developing a mobile app that queries the knowledge stored andintegrated into the PHL in a private and fully decentralized manner to providebetter recommendations. The proposed PHL helps patients and their caregiverstake a central role in making decisions regarding their health and equips theirhealth care providers with informatics tools that support the collection andinterpretation of the collected knowledge.

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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