Sayeed et al report the completion of a digital health research study with an architecture using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard to express the research workflow components, thus introducing the potential for a shared ecosystem for FHIR-based communication of research processes. Beyond the framework offered by these authors, the HL7 FHIR standard has been broadened to include resources for the reporting of research datasets and research findings (evidence), and can now support an architecture for the complete cycle of generation, analysis, dissemination, and application of health research. Making this cycle computable is especially important to reduce the delay in translating research into practice. In 2018, HL7 approved the FHIR Resources for Evidence-Based Medicine Knowledge Assets (EBMonFHIR) project to extend the methods and infrastructure of the HL7 FHIR standard to provide an interoperability standard for the electronic exchange of biomedical knowledge from and about clinical research and recommendations.1 As of 2025, the Evidence Based Medicine on FHIR Implementation Guide (EBMonFHIR IG) defines 90 profiles for FHIR resources for the representation of scientific knowledge and is intended for developers of systems using FHIR for data exchange of scientific knowledge and for authors of more specialized implementation guides in this domain.2

Dear Editors, A 2022 scoping review provided an excellent service by identifying 203 HL7® FHIR®-based tools and initiatives to support clinical research1 but did not report several developments which have not been published extensively in the peer-reviewed literature. An HL7 project started in 2018, Fast Healthcare Interoperability Resources for Evidence-Based Medicine Knowledge Assets (EBMonFHIR), has the goal to provide interoperability (standards for data exchange) for those producing, analyzing, synthesizing, disseminating, and implementing clinical research (evidence) and recommendations for clinical care (clinical practice guidelines).2 FHIR Resources developed or advanced by the EBMonFHIR project that are specifically useful for supporting clinical research include Citation (for the published artifacts), ResearchStudy (for the study record), Evidence (for the study results), EvidenceVariable (for the specification of exposures and outcomes), and ArtifactAssessment (for the comments, ratings, and classifications of knowledge artifacts). These research-supporting resources are available in the Current Development build version of FHIR (FHIR Release #5 at http://build.fhir.org/). The COVID-19 Knowledge Accelerator (COKA) Initiative is an open virtual community with 12 active working groups meeting weekly contributing to the development of standards (particularly FHIR) and tools for the computable expression of evidence and guidance.3,4 EBMonFHIR and COKA projects are freely available on the Fast Evidence Interoperability Resources (FEvIR) Platform at https://fevir.net which is a developing platform to support the creation, viewing, notification, and interaction for computable expression of scientific knowledge, primarily based on the FHIR current build. Free-to-use tools to support automated conversion of structured data reporting clinical research into FHIR Resources include Computable Publishing®: MEDLINE-to-FEvIR Converter at https://fevir.net/medlineconvert, Computable Publishing®: ClinicalTrials.gov-to-FEvIR Converter at https://fevir.net/ctgovconvert, and Computable Publishing®: RIS-to-FEvIR Converter at https://fevir.net/ris. A substantial vocabulary and ontology, the Scientific Evidence Code System (SEVCO), is in development at https://fevir.net/resources/Project/27845. SEVCO is an open project with a global participation and, as of July 22, 2022 has reached unanimous approval of 69 of 70 terms for study design, 121 of 260 terms for risk of bias, and 60 of 235 terms for statistics. An important development for the support of clinical research is development of a standard for the expression of eligibility criteria for research studies. A standard for structured eligibility criteria can facilitate clinical trial matching services used or trial recruitment or to find available studies for a selected patient. Efforts with the HL7 Biomedical Research and Regulation Work Group and an EBMonFHIR Track in an HL7 FHIR Connectathon have resulted in substantial refinement of the EvidenceVariable Resource StructureDefinition (http://build.fhir.org/evidencevariable.html) and demonstrations of “Eligibility Criteria specification with EvidenceVariable” can be found at https://fevir.net/resources/Project/32444. One week after the scoping review was published, another systematic review reported on 49 studies investigating the use of FHIR in health research.5 This is a rapidly developing and important area. We started a project page for “HL7 FHIR-based tools and initiatives to support clinical research” at https://fevir.net/resources/Project/52228 and encourage the scoping review authors or other interested collaborators to maintain a living review of tools and initiatives. BSA contributed the conceptualization and writing of this correspondence. Joanne Dehnbostel, Khalid Shahin, Kenneth Wilkins, Amy Price, and Mario Tristan for feedback on the letter and substantial contributions to the COVID-19 Knowledge Accelerator efforts. BSA is the owner of Computable Publishing LLC and serves primary leadership roles in the not-for-profit EBMonFHIR, COVID-19 Knowledge Accelerator, and Scientific Knowledge Accelerator Foundation efforts.

The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html.

Dear JAMIA Editors, A 2020 perspective article published in Journal of the American Medical Informatics Association (JAMIA) posed a timely question, “Is it time for computable evidence synthesis?”1 The shortest answer is, yes. The novel coronavirus disease 2019 (COVID-19) pandemic poses an immediate demand for evidence synthesis, given that nearly 30 000 articles have been published in fewer than 6 months since that first case in Wuhan, China.2 It provides the informatics community with a unique opportunity to accelerate development and interoperability of many systems to realize the aspirations of computable evidence synthesis. In this letter, we describe the origins and status of the COVID-19 Knowledge Accelerator (COKA). There are tremendous inefficiencies in our current scientific dissemination systems, in which many researchers compute the results then convert the data to various noncomputable forms for human-readable displays, and then many other knowledge processors work with the various human-readable displays to extract the data and enter it into computable form for evidence synthesis. This inefficient pattern is repeated incrementally across multiple steps in an extended series of processes while reports are re-evaluated and reused in subsequent reports. Thus, structured (computable) results directly from research and research publications would greatly accelerate evidence synthesis. Trial registries such as ClinicalTrials.gov are a good place for identifying early system developments for processing structured results data, but structured results data would be especially useful as a companion to scholarly publications, preprint publications, and derivative works in which systematic reviewers and other evidence processors are evaluating currently unstructured results data. For example, COVID-19 studies have already resulted in hundreds of systematic reviews. Achieving a state of structured results data as standard practice will not likely occur through a single universal repository, but we believe that it can be achieved with universal standards for data exchange, and multiple component standards that account for the many types of data that represent and support research results. Several groups—including the Guidelines International Network—seeking to accelerate evidence synthesis through collaborations set out to define standards for computable expressions of evidence, statistics, and evidence variables. In 2018, we started a project through Health Level Seven (HL7) International to extend the Fast Healthcare Interoperability Resources (FHIR) standard to achieve this. The group is called Evidence-Based Medicine on FHIR (EBMonFHIR).3 In less than 2 years, the EBMonFHIR project established draft standards for expression of evidence (http://build.fhir.org/evidence.html), evidence variables (http://build.fhir.org/evidencevariable.html), Statistics (http://build.fhir.org/statistic.html), and ordered distributions for statistical arrays (http://build.fhir.org/ordereddistribution.html). Recent developments to overcome the COVID-19 pandemic have stimulated many researchers, scholars, and information professionals to initiate large consortium-based efforts to share their work and advance our knowledge of the virus and the pandemic. Examples include the COVID-19 Open Research Dataset (CORD-19) (https://cset.georgetown.edu/research/covid-19-open-research-dataset-cord-19/), the COVID-19 Evidence Network to support Decision-making (COVID-END) (https://www.mcmasterforum.org/networks/covid-end), and the Australian National Clinical Evidence Taskforce (https://covid19evidence.net.au/). Several of these consortia asked to leverage EBMonFHIR efforts to provide standards for interoperable evidence syntheses. In response to these requests, our group initiated COKA (https://www.gps.health/covid19_knowledge_accelerator.html). The specific strategy of COKA is to establish universal standards for each component of knowledge exchanged and thus enable stakeholders to share and reuse their efforts by using the same format for electronic data exchange. As of May 11, 2020, COKA had 50 working meetings with more than 40 active participants from more than 25 organizations from academia, industry, government, and nonprofits in 7 countries. The group has created additional draft FHIR standards for expressions of citations (http://build.fhir.org/citation.html) and evidence reports (http://build.fhir.org/evidencereport.html) that provide compositions of all the preceding concepts. We strongly encourage developers of systems for evidence identification, evaluation, and dissemination to use these resources now as foundational elements to create a computational evidence ecosystem. This environment includes the building blocks for achieving computable evidence synthesis. Other resources not noted previously, such as resources for computational logic expressions, may ultimately be needed for the complete ecosystem. If we can develop standards for each granular component, we can then weave together the many overlapping systems and consortia to accelerate realization of this complex evidence ecosystem. Computable evidence synthesis is not the endpoint, but rather is another step in a larger knowledge ecosystem. For instance, the EBMonFHIR project is closely related to a CPGonFHIR project (http://build.fhir.org/ig/HL7/cqf-recommendations/) extending FHIR to support clinical guidelines. Past and present consortia efforts that have or are considering advancements for this ecosystem include the Agency for Healthcare Research and Quality evidence-based Care Transformation Support (ACTS) initiative (https://digital.ahrq.gov/acts), the Centers for Disease Control and Prevention's Adapting Clinical Guidelines for the Digital Age (https://www.cdc.gov/ddphss/clinical-guidelines/), Logica (https://covid-19-ig.logicahealth.org/), Mobilizing Computable Biomedical Knowledge (MCBK) (http://mobilizecbk.org/), and the Patient-Centered Clinical Decision Support Learning Network (PCCDS LN) (https://pccds-ln.org/). Although creating a computational environment for evidence could be done for any domain or subject matter,4,5 COVID-19 currently presents a unique human interest with urgency and impact, thus providing a special openness to collaboration. We invite your participation at https://www.gps.health/covid19_knowledge_accelerator.html. VS is supported in part by the National Science Foundation under grant #1838745 and the Arizona Board of Regents’ Technology Research and Innovation Fund. All listed authors contributed to this correspondence. BSA was employed by EBSCO Information Services and owns Computable Publishing LLC but the COKA and EBMonFHIR efforts are open, noncommercial activities. The other authors have no competing interests to report.