BackgroundSemantic interoperability remains a significant barrier in healthcare, particularly when integrating patient-reported, clinical, and genomic data to enable personalized care. Existing models rarely focus on patient-centered, ontology-driven front-end architectures based on widely adopted standardized medical ontologies and terminologies. Within broader Personal Health Data Space (PHDS) initiatives, such integration increasingly depends on front-end frameworks that enable semantic consistency and patient-centered usability across heterogeneous clinical domains and systems.ObjectiveThis analysis presents a review-informed framework to support semantic integration, data governance, user experience, and patient engagement. The objective is to present a front-end, standards-aligned, ontology-driven model grounded in established healthcare standards.MethodsBased on our previously published systematic review and thematic synthesis, this paper presents a review-informed conceptual framework. It outlines a modular front-end architecture for semantic healthcare data integration. The framework was developed through a reproducible synthesis-to-design process, consistent with design science principles of treatment design, thereby ensuring conceptual rigor and alignment with evidence. Using a knowledge-based modeling approach, we designed a six-layer architecture comprising User Experience, Security and Compliance, Data Management, Interoperability and Integration, Advanced Analytics, and Support and Scalability. Each layer is aligned with established standards including Health Level Seven—Fast Healthcare Interoperability Resources (HL7 FHIR), Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT), and Logical Observation Identifiers Names and Codes (LOINC), compliance with privacy and security regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).ResultsThe framework illustrates how ontologies and health IT standards can be conceptually incorporated within front-end system design to unify structured and unstructured data, providing a foundation for secure sharing and standards-aligned integration with existing health information systems.ConclusionsThis review-informed analysis introduces the Self Data Atlas Front-End Framework (SDA-FEF), an ontology-driven, standards-aligned Electronic Health Record (EHR) front-end architecture designed to support patient-centered care. By promoting semantic interoperability, structured data integration, and user-centered design, the framework conceptually advances the development of healthcare systems that may enhance continuity of care and overall quality of life.
Development and Evaluation of a Hallucination Awareness Scale for Healthcare Professionals and its impact on diagnostic confidence
Generative artificial intelligence (Gen AI) has gained immense significance in recent years, particularly in the field of healthcare. Despite its significant role in streamlining healthcare-related



