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Opportunities and Challenges for Designing in Connected Health: Insights From an Expert Workshop
Implementing resource-based architectures, supported by frameworks such as Fast Healthcare Interoperability Resources (FHIR), ensures that systems can scale effectively to handle increasing data volumes. Additionally, these infrastructures provide opportunities for integrating tools such as predictive analytics and decision-support systems, further enhancing their utility. However, the success of digital infrastructures relies on adherence to standards for data security, privacy, and interoperability.
J Med Internet Res 2025;27:e72446
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DermaDashboard: Bridging the Gap Between FHIR Standards and Clinical Usability
In recent years, health care systems have rapidly embraced digitalization to enhance patient care, streamline clinical workflows, and improve interoperability of health information [1-4]. At the heart of this transformation is the widespread adoption of electronic health records (EHRs) [5], which, on a broader scale, support the secondary use of data beyond clinical aspects, such as quality control, safety measurements, payments, certification, and research [6,7].
JMIR Cancer 2025;11:e73691
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Healthcare interoperability standards such as the Fast Healthcare Interoperability Resources (FHIR) [1] are providing new methodological approaches for the collection, collation, and confirmation of clinical research data for both observational studies and trials supporting product regulatory submissions [2-4]. To be successful, clinical research studies require that (1) the correct data are available to answer the research question and (2) these data are collected at the correct times.
JMIR Med Inform 2025;13:e71430
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This observation shows the importance of well-made implementation guides (IGs) focusing on well-defined medical areas, such as NCD prevention, and using current interoperability standards. The framework suggests using standards, but no IGs or interoperability profiles are provided.
Yusoff et al [23] developed a conceptual framework for serious learning games. It combines learning and pedagogy theory with gaming requirements.
JMIR Serious Games 2025;13:e69246
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Within this strategy, the UK Health Security Agency (UKHSA) aims to prioritize FAIR (findability, accessibility, interoperability, and reusability) principles [11]. In the context of FAIR, interoperability is defined as “the data need to interoperate with applications or workflows for analysis, storage, and processing.”
JMIR Hum Factors 2025;12:e66398
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Through integration with the e Referral platform using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR), the BAW platform will receive the pdf package, including the referral details and the required fax number [14-16]. The BAW platform will then automate the process following a predefined workflow and deliver information to the fax-based receiving clinician through the second integration with the fax service platform.
JMIR Med Inform 2025;13:e62983
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Several studies emphasized the necessity of standard terminologies and ontologies to enhance data consistency and interoperability. In addition, multiple sources explored different machine learning techniques, such as supervised and unsupervised learning approaches, to detect and mitigate issues related to incomplete or inconsistent data. Key insights from the literature were incorporated into the introduction and methodology sections, providing a solid theoretical foundation for this study.
JMIR Med Inform 2025;13:e60204
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Both PROMIS and the CDE repository are essential for enhancing the interoperability of health data.
In practice, the deployment of PGHD acquisition applications requires that survey questions be drawn from these established standardized resources. Data collected using questions outside of these resources still require additional efforts to achieve standardization.
JMIR Med Inform 2025;13:e71687
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Previous reviews on interoperability have not focused on specific industries such as health care, nor have they consistently provided structured classifications. One review addressed user model interoperability but lacked a systematic approach [17]. Another study examined evaluation models of interoperability, categorizing 12 models across 4 granularity levels [10]. While this study mentioned interoperability attributes, these were not developed through a systematic procedure [10].
J Med Internet Res 2025;27:e69465
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To facilitate this exchange, HIE requires a data exchange or data interoperability framework that provides a standardized format for transferring health data [2]. One of the organizations that provides such interoperability frameworks is Health Level Seven (HL7) [3]. HL7 is a nonprofit organization that develops interoperability frameworks, such as HL7 FHIR (Fast Healthcare Interoperability Resources). It is a newer standard that provides a modern and flexible approach to exchanging health information.
JMIR Form Res 2025;9:e51270
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