The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .
Objective
Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps’ clinical, technical, and implementation characteristics.
Materials and Methods
We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders.
Results
Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery.
Discussion
Though limited in scope, our results show FHIR apps encompass various domains and characteristics.
Conclusion
As FHIR use expands, this study—one of the first to characterize FHIR apps at large—highlights the need for systematic, comprehensive methods to assess their characteristics.
SummaryTo celebrate over 30 years of health information systems’ (HIS) evolution by bringing together pioneers in the field, members of the next generation of leaders, and government officials from several developing nations in Africa to discuss the past, present, and future of HISs.Participants gathered in Le Franschhoek, South Africa for a 2 ½ day working conference consisting of scientific presentations followed by several concurrent breakout sessions. A small writing group prepared draft statements representing their positions on various topics of discussion which were circulated and revised by the entire group.Many new tools, techniques and technologies were described and discussed in great detail. Interestingly, all of the key themes identified in the first HIS meeting held over 30 years ago are still of vital importance today: Patient Centered design, Clinical User Support, Real-time Education, Human-computer Factors and Measuring Clinical User Performance, Meaningful use.As we continue to work to develop next-generation HISs, we must remember the lessons of the past as we strive to develop the solutions for tomorrow.
SummaryObjectives: Describe how the HL7 Clinical Document Architecture (CDA), a foundational standard in US Meaningful Use, contributes to a "big data, incrementally structured" interoperability strategy, whereby data structured incrementally gets large amounts of data flowing faster. We present cases showing how this approach is leveraged for big data analysis. Methods: To support the assertion that semi-structured narrative in CDA format can be a useful adjunct in an overall big data analytic approach, we present two case studies. The first assesses an organization's ability to generate clinical quality reports using coded data alone vs. coded data supplemented by CDA narrative. The second leverages CDA to construct a network model for referral management, from which additional observations can be gleaned.Results: The first case shows that coded data supplemented by CDA narrative resulted in significant variances in calculated performance scores. In the second case, we found that the constructed network model enables the identification of differences in patient characteristics among different referral work flows. Discussion: The CDA approach goes after data indirectly, by focusing first on the flow of narrative, which is then incrementally structured. A quantitative assessment of whether this approach will lead to a greater flow of data and ultimately a greater flow of structured data vs. other approaches is planned as a future exercise. Conclusion: Along with growing adoption of CDA, we are now seeing the big data community explore the standard, particularly given its potential to supply analytic engines with volumes of data previously not possible.
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