Fast Healthcare Interoperability Resources
|Base standards||JSON, XML, RDF|
|Domain||Electronic health records|
Fast Healthcare Interoperability Resources (FHIR, pronounced "fire") is a standard describing data formats and elements (known as "resources") and an application programming interface (API) for exchanging electronic health records (EHR). The standard was created by the Health Level Seven International (HL7) health-care standards organization.
FHIR builds on previous data format standards from HL7, like HL7 version 2.x and HL7 version 3.x. But it is easier to implement because it uses a modern web-based suite of API technology, including a HTTP-based RESTful protocol, HTML and Cascading Style Sheets for user interface integration, a choice of JSON, XML or RDF for data representation, and Atom for results. One of its goals is to facilitate interoperation between legacy health care systems, to make it easy to provide health care information to health care providers and individuals on a wide variety of devices from computers to tablets to cell phones, and to allow third-party application developers to provide medical applications which can be easily integrated into existing systems.
FHIR provides an alternative to document-centric approaches by directly exposing discrete data elements as services. For example, basic elements of healthcare like patients, admissions, diagnostic reports and medications can each be retrieved and manipulated via their own resource URLs. FHIR was supported at an American Medical Informatics Association meeting by many EHR vendors which value its open and extensible nature.
In December 2014, a broad cross-section of stakeholders committed to the Argonaut Project which will provide acceleration funding and political will to publish FHIR implementation guides and profiles for query/response interoperability and document retrieval by May 2015. It would then be possible for medical records systems to migrate from the current practice of exchanging complex Clinical Document Architecture (CDA) documents, and instead exchange sets of simpler, more modular and interoperable FHIR JSON objects. The initial goal, to be completed in time for the May 2015 HL7 FHIR Draft Standard for Trial Use revision 2 ballot, is to specify two FHIR profiles that are relevant to the Meaningful Use requirements, along with an implementation guide for using OAuth 2.0 for authentication. 
The CEO of HL7 argued in August 2016 that it already provided valuable features, was ready to use, and was being adopted.
FHIR Release 3 was published in March 2017, as the first STU (Standards for Trial Use) release. It included coverage of a variety of clinical workflows, a Resource Description Framework format, and a variety of other updates.
FHIR Release 4.0.1 was published on October 30, 2019.
FHIR is organized by resources (e.g., patient, observation). Such resources can be specified further by defining FHIR profiles (for example, binding to a specific terminology). A collection of profiles can be published as an implementation guide (IG), such as The U.S. Core Data for Interoperability (USCDI).
A number of high-profile players in the health care informatics field are showing interest in and experimenting with FHIR, including CommonWell Health Alliance and SMART (Substitutable Medical Applications, Reusable Technologies). In 2014, the U.S. Health IT Policy and the Health IT Standards committees endorsed recommendations for more public (open) APIs. The JASON task force report on "A Robust Health Data Infrastructure" says that FHIR is currently the best candidate API approach, and that such APIs should be part of stage 3 of the "meaningful use" criteria of the U.S. Health Information Technology for Economic and Clinical Health Act.
A variety of applications were demonstrated at the FHIR Applications Roundtable in July 2016. The Sync for Science (S4S) profile builds on FHIR to help medical research studies ask for (and if approved by the patient, receive) patient-level electronic health record data.
A collaboration agreement with Healthcare Services Platform Consortium (now called Logica) was announced in 2017. Experiences with developing medical applications using FHIR to link to existing electronic health record systems clarified some of the benefits and challenges of the approach, and with getting clinicians to use them.
In January, 2018, Apple announced that its iPhone Health (Apple) App would allow viewing a user's FHIR-compliant medical records when providers choose to make them available. Johns Hopkins Medicine, Cedars-Sinai, Penn Medicine, NYU-Langone Medical Center, Dignity Health and other large hospital systems participated at launch.
In 2020, the U.S. Centers for Medicare & Medicaid Services (CMS) issued their Interoperability and Patient Access final rule, requiring use of FHIR by a variety of CMS-regulated payers, including Medicare Advantage, organizations, state Medicaid programs, and qualified health plans in the Federally Facilitated Marketplace by 2021.☃☃In 2020, the U.S. Centers for Medicare & Medicaid Services (CMS) issued their Interoperability and Patient Access final rule, requiring use of FHIR by a variety of CMS-regulated payers, including Medicare Advantage, organizations, state Medicaid programs, and qualified health plans in the Federally Facilitated Marketplace by 2021.
A software package that implements a large number of FHIR features can be referenced as FHIR server.
In 2020, the U.S. Centers for Medicare & Medicaid Services (CMS) issued their Interoperability and Patient Access final rule, requiring use of FHIR by a variety of CMS-regulated payers, including Medicare Advantage, organizations, state Medicaid programs, and qualified health plans in the Federally Facilitated Marketplace by 2021.
Implications for Health Informatics
Because FHIR is implemented on top of HL7 and the HTTPS (HTTP Secure) protocol, messages can be parsed by wire data analytics platforms for real-time data gathering. In this concept, healthcare organizations would be able to gather real-time data from specified segments in FHIR messages as those messages pass over the network. That data can be streamed to a data store where it can be correlated with other informatics data. Potential use cases include epidemic tracking, prescription drug fraud, adverse drug interaction warnings, and emergency room wait times.
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