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Open Ecosystem Through Secure Plug and Play Interoperability: An Overview.
J Diabetes Sci Technol
January 2025
Roche Diabetes Care GmbH, Mannheim, Germany.
Background: Interoperability is a critical enabler for integrated Personalized Diabetes Management (iPDM), automated insulin delivery (AID), and the digital transformation of healthcare in general. However, manufacturers still create closed ecosystems (ie, solutions designed to work end-to-end minimizing collaboration with other organizations) with proprietary interfaces because of various interoperability challenges. Therefore, the aim of this article is to provide an overview of how to achieve organizational interoperability in an open ecosystem (ie, solutions designed to integrate different organizations via interoperability standards) for diabetes management.
View Article and Find Full Text PDFIntegration of pharmacogenetic data in epic genomic module drives clinical decision support alerts.
Front Pharmacol
December 2024
Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.
Introduction: The Precision Medicine Program (PMP) at the University of Florida (UF) focuses on advancing pharmacogenomics (PGx) to improve patient care.
Methods: The UF PMP, in collaboration with the UF Health Pathology Laboratory (UFHPL), utilized Health Level Seven (HL7) standards to integrate PGx data into Epic's Genomic Module to enhance the management and utilization of PGx data in clinical practice.
Results: A key feature of the Genomic Module is the introduction of genomic indicators-innovative tools that flag actionable genetic information directly within the electronic health record (EHR).
Patient-Centric Approach to Personalized Electronic Medical Records via QR Code in Japan.
Interact J Med Res
December 2024
Life Science Laboratories, KDDI research atelier, KDDI Research, Inc, Fujimino, Saitama, Japan.
Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information.
View Article and Find Full Text PDFCross-Standard Health Data Harmonization using Semantics of Data Elements.
Sci Data
December 2024
Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, 1105, AZ, The Netherlands.
Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards.
View Article and Find Full Text PDFTackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.
Lancet Digit Health
January 2025
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK; Centre for Patient Reported Outcomes Research, School of Health Sciences, College of Medical and Dental Sciences, Birmingham, UK; University of Birmingham, Birmingham, UK. Electronic address:
Article Synopsis
- There is a significant risk of reinforcing existing health inequalities in AI health technologies due to biases, primarily stemming from the datasets used.
- The STANDING Together recommendations focus on transparency in health datasets and proactive evaluation of their impacts on different population groups, informed by a comprehensive research process with over 350 global contributors.
- The 29 recommendations are divided into guidance for documenting health datasets and strategies for using them, aiming to identify and reduce algorithmic biases while promoting awareness of the inherent limitations in all datasets.
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