FHIR PIT: a geospatial and spatiotemporal data integration pipeline to support subject-level clinical research

• Published in: BMC medical informatics and decision making Vol. 25; no. 1; pp. 24 - 11
• Main Authors: Fecho, Karamarie, Garcia, Juan J., Yi, Hong, Roupe, Griffin, Krishnamurthy, Ashok
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.01.2025; BioMed Central Ltd; BMC
• Subjects: Air pollution; Airborne pollutant exposures; Asthma; Biomedical Research - methods; Data integration; Datasets; Disorders; Electronic Health Records; Electronic medical records; Emergency medical care; Emergency medical services; Environmental Exposure; Environmental exposures; Environmental health; Environmental research; Exposure; Health aspects; Health Informatics; Health Information Interoperability; HL7® FHIR; Humans; Indoor air pollution; Information Systems and Communication Service; Integration; Lung diseases; Management of Computing and Information Systems; Medical informatics; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Methods; Open source software; Outdoor air quality; Ozone; Particulate emissions; Particulate matter; Patients; Pollutants; Primary ciliary dyskinesia; Privacy; Software; Spatio-Temporal Analysis; Spatiotemporal data; United States; United States > US; Primary ciliary dyskinesia; Socioeconomic exposures; Data integration; Airborne pollutant exposures; Hospital visits; Environmental exposures; HL7® FHIR; Asthma
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02815-6

Background Environmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record (EHR) data or other sources of clinical data such as survey data. While numerous public sources of geospatial and spatiotemporal data are available to support such research, the data are challenging to work with due to inconsistencies in file formats and spatiotemporal resolutions, computational challenges with large file sizes, and a lack of tools for patient- or subject-level data integration. Results We developed FHIR PIT (HL7® Fast Healthcare Interoperability Resources Patient data Integration Tool) as an open-source, modular, data-integration software pipeline that consumes EHR data in FHIR® format and integrates the data at the level of the patient or subject with environmental exposures data of varying spatiotemporal resolutions and file formats. We applied FHIR PIT to generate “integrated feature tables” containing patient- or subject-level EHR data integrated with environmental exposures data on two cohorts: one on patients with asthma and related common pulmonary disorders; and a second on patients with primary ciliary dyskinesia and related rare pulmonary disorders. The data were then exposed via the open Integrated Clinical and Environmental Exposures Service, which was then queried to explore relationships between exposures to two representative airborne pollutants (particulate matter and ozone) and annual emergency department or inpatient visits for respiratory issues. We found that hospitalizations for respiratory issues were more common among patients exposed to relatively high levels of particulate matter and ozone and were higher overall among patients with primary ciliary dyskinesia than among patients with asthma. Conclusions Our manuscript describes a major release of FHIR PIT v1.0 and includes a technical demonstration use case and a clinical application on the use of FHIR PIT to support research on environmental exposures and health outcomes related to asthma and primary ciliary dyskinesia. For application of the tool to common data models (CDMs) other than FHIR, we offer open-source conversion tools to map from the PCORnet, i2b2, and OMOP CDMs to FHIR.