Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.

With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR® for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR® standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR® (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are "not mapped" will enable us to improve the standard and develop profiles that will better fit the registry data model.

Comparing the Accuracy of Traditional vs. FHIR®-Based Extraction of Electronic Health Record Data for Two Clinical Trials.

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.