Document Type

Theses, Ph.D

Abstract

With the advent of Patient-Generated Health Data (PGHD) through wearable, mobile, and home monitoring systems, there is immense potential for ongoing monitoring and patient engagement. But integrating PGHD with Electronic Health Record (EHR) is challenged by sub-optimal support for contextual metadata and its relevant elements, lack of semantic interoperability among disparate systems, poor knowledge regarding the factors that impact clinician acceptance, and absence of globally agreed standards for data exchange. This thesis explores how contextually relevant patient-generated health data can be shared with EHRs through a FAIR standardized information model that ensures semantic and syntactic interoperability.

The study addresses six objectives. First, a systematic narrative review highlights the shortcomings of current PGHD-EHR integration strategies, especially concerning the representation of provenance and the capture of contextual metadata. Second, an information model is developed, addressing the conceptual, semantic, and syntactic aspects. The conceptual model, based on the W3C Proposed Recommendation for the PROV-O reference model, captures the entities, events, and agents involved in PGHD sharing and explicitly describes the contextual aspects (measurement protocols, states of patients, care programmes, and quality scores). Third, two formal PGHD-specific ontologies, PGHDProvO and WearPGHDProvO, are developed, which enable machine-actionable representations. Fourth, a HL7 FHIR Implementation Guide is developed for translating the concept and semantic models into implementable specifications. Fifth, a qualitative study of clinician perspectives on PGHD value and the role of provenance explores how PGHD utility and provenance value are perceived. Finally, a systematic evaluation of the proposed ontologies using the FOOPs! tool ii shows a very high alignment score (90%) with Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

The clinician study has major theoretical implications, showing that provenance and context are levers for the use of PGHD in the application of clinical decision-making. Additionally, the study shows which clinical decision tasks are most likely to benefit most from PGHD. Practical implications of the research include the availability of PGHD provenance ontologies that are FAIR (https://w3id.org/pghdprovo and https://w3id.org/wearpghdprov) and the comprehensive PGHD FHIR Implementation Guide - https://w3id.org/pghdprovo/fhir. Overall, this thesis has contributed to the development of novel, practical solutions to the PGHD-EHR integration problem and the generation of transferable knowledge to the field of health informatics and human computer interaction.

DOI

https://doi.org/10.21427/4p5d-3318

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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