474 Innovative solutions to streamline data collection, exchange, and utilization in translational research

Abstract

OBJECTIVES/GOALS: To determine the utility of any standard, one must first evaluate whether or not the standard meets the needs of the use case it is meant to support. We aim to quantify data availability of the HL7 FHIR standard to support data collection for three state-based registries in a rural state and measure the potential effects on data quality and collection time. METHODS/STUDY POPULATION: FHIR mapping will be performed to assess the level of HL7 FHIR standard completeness (or data element coverage) in supporting data collection for the three registries. A systematic approach, previously developed and used by Garza and Zozus, will be used to map registry data elements to corresponding HL7 FHIR standard resources. FHIR coverage will be calculated as a percentage (total data elements “Available in FHIR” vs. total data elements overall) and will be observed across different domain areas (i.e., demographics vs. medications vs. vital signs, etc.) in order to identify the domains with the least and most coverage. RESULTS/ANTICIPATED RESULTS: Although there have been informatics solutions that relied on data exchange standards to improve data collection, none have actually evaluated the coverage of the standard for supporting the needs of clinical data registries. To address this gap, we aim to evaluate the availability of the HL7 FHIR standard to support data collection for three state-based registries. These results will provide insight into the generalizability of a FHIR-based solution to support data acquisition and processing across multiple registries and demonstrate the potential for seamless exchange of that data for secondary use in clinical and translational research. Quantifying the coverage will also be used to further advance its development in order to meet the data collection needs of state and national clinical data registries. DISCUSSION/SIGNIFICANCE: Registries often rely on manual abstraction of EHR data. These manual approaches have had a negative impact on data quality and cost, often due to the complexities associated with collection and mapping of the data to fit the registry model. HL7 FHIR has the potential to address these issues by automating part or all of the data collection process.