After years of giving lip service to standards, the health information/informatics community is now taking interoperability very seriously. The reason is obvious: we have spent a decade ramping up adoption of electronic health records (EHRs) in the US
and elsewhere, and we have learned in hindsight that in the hurry to get systems implemented as quickly and easily as possible, inadequate attention was paid to data standards and interoperability. As a result, we have EHRs across the health system that do not easily talk to each other. The has compromised the hopes we have had for simple health information exchange (HIE) , not to mention the myriad re-uses of EHR data for research and quality assurance data .
The adoption of standards was also hindered by problems with the existing HL7 messaging standards, from the venerable but limited Version 2 to the hopelessly complex Version 3. Then, being the right solution at the right time, along came the Fast Healthcare Interoperability Resources (FHIR) standard
Demonstrating a strong need for improved interoperability, the uptake across healthcare has been swift. FHIR received an early boost by being synergistic with the SMART app framework . Another early enthusiast was ONC, which baked FHIR into the new rules mandated from the 21st Century Cures Act
. (I view the health IT aspects of this legislation as a way to clean up the insufficient attention to interoperability in the original HITECH Act).
Other communities have jumped on the bandwagon as well. The new push to make quality measures easier to extract from the EHR, as opposed to requiring manual extraction from chart reviews, in the electronic Clinical Quality Measures (eCQMs) project
. The research community has joined as well, with the National Institutes of Health (NIH) calling for its use
. Other research groups have started to develop tools, such as in natural language processing (NLP) pipelines  and the Clinical Data to Health (CD2H) data coordinating center
for the Clinical & Translational Science Award (CTSA) program.
Other uptake of FHIR includes its use in clinical decision support tools
. The value-based care community has jumped on board as well in the Da Vinci Project
that focuses on managing and sharing clinical and administrative data. There are even applications in the education arena, with the description of a tool recently developed to use SMART on FHIR in a case-based learning situation .
Despite all the excitement and achievement, the operational use of FHIR remains modest. This is not to argue that it will not achieve success across the healthcare system, but at the present time its use in real-world situations is small. But the enthusiasm shows that the needs it is intended to address are real, and that it has the potential to provide effective solutions for interopreability problems.
One example of a great start but need for more development involves the Apple Health app on my iPhone. I love to pull out my phone and show anyone who wants to see that I can download a good portion of my medical record from my institution's Epic EHR system to the Apple Health app, even the function that lets me show the FHIR resources in raw XML. But the reality is that at the present time, about all I can do with this app is show the presence of my data to people.
There are still some unanswered questions about making FHIR operational, such as:
- How will the FHIR approach scale up to large and diverse data types from the EHR and beyond?
- Will we be able to transform the unstructured data in notes and other parts of the record into the detailed structured form of FHIR?
- How will people manage the data that leaves the healthcare system, especially consumers who may not be savvy about their medical data they now possess on their phones and other devices?
We therefore need to think at the present time of FHIR as more aspirational than operational. That said, we need to leverage the widespread enthusiasm across the healthcare system to build on the current foundation to carry out the real work that must be done to reach the goals that everyone has for standards-based, interoperable data.
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2. Meystre S, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann C. Clinical data reuse or secondary use: current status and potential future progress. In: Holmes J, Soualmia L, Séroussi B, editors. Yearbook of Medical Informatics. 262017. p. 38-52.
3. Benson T, Grieve G. Principles of Health Interoperability - SNOMED CT, HL7 and FHIR, Third Edition. London, England: Springer; 2016.
4. Braunstein M. Health Informatics on FHIR: How HL7's New API is Transforming Healthcare. New York, NY: Springer; 2018.
5. Mandel J, Kreda D, Mandl K, Kohane I, Ramoni R. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association. 2016;23:899-908.
6. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, et al. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data JAMIA Open. 2019: Epub ahead of print.
7. Braunstein M, Oancea I, Barry B, Darlington S, Steel J, Hansen D, et al. The development and electronic delivery of case-based learning using a Fast Healthcare Interoperability resource system. JAMIA Open. 2019: Epub ahead of print.