FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

Abstract

Background: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease. Results: We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations. Conclusions: While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.

Translator Exposure APIs: Open Access to Data on Airborne Pollutant Exposures, Roadway Exposures, and Socio-Environmental Exposures and Use Case Application

“…We successfully used the open APIs to extract exposures data on 100% of geocoded participants within an EPR cohort, and we integrated the exposures data with EPR data at the participant level. Importantly, we applied the data to a proof-of-concept asthma use case and demonstrated an association between asthma exacerbations, as measured by participant self-report of ED or urgent care visit for asthma, and sex, race, smoking history, obesity, median household income, and exposure to airborne particulate matter, thus largely supporting our hypothesis that the Translator Exposures APIs could be used to replicate our prior findings [24][25][26]35,36].…”

“…To facilitate comparison with prior EPR findings on participants with self-reported asthma [35] and results from ICEES on patients with asthma-like conditions as documented in electronic health records [24][25][26]36], we focused our preliminary analysis on an EPR subcohort of n = 932 participants with a self-reported diagnosis of asthma, of which n = 923 participants had data on the primary endpoint measure of self-reported ED or urgent care visits for asthma (one or more in prior 12 months), and n = 879 of those had geocodes and therefore had exposures data available from the Translator Exposures APIs. We explored the impact of select demographic features and environmental exposures on asthma exacerbations, chosen again to facilitate comparison with our prior work: sex (from EPR); race (from EPR); history of smoking (from EPR); obesity (from EPR); exposure to PM 2.5 (from Airborne Pollutant Exposures API) exposure to a major roadway or highway (from Roadway Exposures API); and estimated median household income exposure (from Socio-environmental Exposures API).…”

“…The variables that were selected were representative of each of the data sources (i.e., EPR data, Airborne Pollutant Exposures API, Roadway Exposures API, Socio-environmental Exposures API). Importantly, they were also chosen to replicate and extend our prior work [24][25][26]35,36], which focused on associations between asthma exacerbations and sex, race, history of smoking, obesity, airborne particulate matter exposure, major roadway or highway exposure, and estimated household income. We used those same variables for the demonstration project reported here.…”

“…For research and development of the Translator Exposures APIs, we focused on 2550 study participants who were successfully geocoded from a total of 4130 total participants in the EPR cohort on inflammation and respiratory conditions. For use case analysis and comparison with prior results [24][25][26]35,36], we focused on a subset of the cohort with a self-reported diagnosis of asthma (described below and in greater detail in the Results section, under Use Case Results and Table 2).…”

“…As part of this work, we developed several open tools and services that provide users with easy access to processed and computable data, including the Integrated Clinical and Environmental Exposures Service (ICEES). ICEES is a Translator service that provides open access to electronic health record data that have been integrated at the individual level with a variety of public exposures data [24][25][26]. ICEES is currently focused on environmental influences on asthma-like conditions, but the service itself was developed as a disease-agnostic framework and approach for openly exposing integrated clinical and environmental exposures data.…”