1. Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.
- Author
-
Bikkanuri, Manju, Robins, Taiquitha T., Wong, Lori, Seker, Emel, Greer, Melody L., Williams, Tremaine B., and Garza, Maryam Y.
- Subjects
- *
DATA transmission systems standards , *WOUNDS & injuries , *RESEARCH funding , *REPORTING of diseases , *DESCRIPTIVE statistics , *CONTENT mining , *PUBLIC health , *HEALTH facilities , *STROKE , *QUALITY assurance ,ELECTRONIC health record standards - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF