Suitability of fast healthcare interoperability resources (FHIR) for wellness data

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Abstract

Wellness data generated by patients using smart phones and portable devices can be a key part of Personal Health Record (PHR) data and offers healthcare service providers (healthcare providers) patient health information on a daily basis. Prior research has identified the potential for improved communication between healthcare provider and patient. However the practice of sharing patient generated wellness data has not been widely adopted by the healthcare sector; one of the reasons being the lack of interoperability preventing successful integration of such device generated data into the PHR and Electronic Health Record (EHR) systems. To address the interoperability issue it is important to make sure that wellness data can be supported in healthcare information exchange standards. Fast Healthcare Interoperability Resources (FHIR) is used in the current research study to identify the technical feasibility for patient generated wellness data. FHIR is expected to be the future healthcare information exchange standard in the healthcare industry. A conceptual data model of wellness data was developed for evaluation using FHIR standard. The conceptual data model contained blood glucose readings, blood pressure readings and Body Mass Index (BMI) data and could be extended to accept other types of wellness data. The wellness data model was packaged in an official FHIR resource called Observation. The research study proved the flexibility of adding new data elements related to wellness in Observation. It met the requirements in FHIR to include such data elements useful in self-management of chronic diseases. It also had the potential in sharing it with the healthcare provider system.

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APA

Pais, S., Parry, D., & Huang, Y. (2017). Suitability of fast healthcare interoperability resources (FHIR) for wellness data. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vol. 2017-January, pp. 3499–3505). IEEE Computer Society. https://doi.org/10.24251/hicss.2017.423

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