» Articles » PMID: 32160884

FHIR PIT: an Open Software Application for Spatiotemporal Integration of Clinical Data and Environmental Exposures Data

Overview
Publisher Biomed Central
Date 2020 Mar 13
PMID 32160884
Citations 8
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Causal analysis for multivariate integrated clinical and environmental exposures data.

Sinha M, Haaland P, Krishnamurthy A, Lan B, Ramsey S, Schmitt P BMC Med Inform Decis Mak. 2025; 25(1):27.

PMID: 39815256 PMC: 11736916. DOI: 10.1186/s12911-025-02849-4.


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

Fecho K, Garcia J, Yi H, Roupe G, Krishnamurthy A BMC Med Inform Decis Mak. 2025; 25(1):24.

PMID: 39810200 PMC: 11734467. DOI: 10.1186/s12911-024-02815-6.


FHIR-PYrate: a data science friendly Python package to query FHIR servers.

Hosch R, Baldini G, Parmar V, Borys K, Koitka S, Engelke M BMC Health Serv Res. 2023; 23(1):734.

PMID: 37415138 PMC: 10326955. DOI: 10.1186/s12913-023-09498-1.


An approach for open multivariate analysis of integrated clinical and environmental exposures data.

Fecho K, Haaland P, Krishnamurthy A, Lan B, Ramsey S, Schmitt P Inform Med Unlocked. 2022; 26.

PMID: 35875189 PMC: 9302917. DOI: 10.1016/j.imu.2021.100733.


Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review.

Vorisek C, Lehne M, Klopfenstein S, Mayer P, Bartschke A, Haese T JMIR Med Inform. 2022; 10(7):e35724.

PMID: 35852842 PMC: 9346559. DOI: 10.2196/35724.


References
1.
Rabinovitch N, Adams C, Strand M, Koehler K, Volckens J . Within-microenvironment exposure to particulate matter and health effects in children with asthma: a pilot study utilizing real-time personal monitoring with GPS interface. Environ Health. 2016; 15(1):96. PMC: 5057244. DOI: 10.1186/s12940-016-0181-5. View

2.
Ahalt S, Chute C, Fecho K, Glusman G, Hadlock J, Taylor C . Clinical Data: Sources and Types, Regulatory Constraints, Applications. Clin Transl Sci. 2019; 12(4):329-333. PMC: 6617834. DOI: 10.1111/cts.12638. View

3.
Jagai J, Krajewski A, Shaikh S, Lobdell D, Sargis R . Association between environmental quality and diabetes in the USA. J Diabetes Investig. 2019; 11(2):315-324. PMC: 7078099. DOI: 10.1111/jdi.13152. View

4.
Saha C, Riner M, Liu G . Individual and neighborhood-level factors in predicting asthma. Arch Pediatr Adolesc Med. 2005; 159(8):759-63. DOI: 10.1001/archpedi.159.8.759. View

5.
Kramer U, Herder C, Sugiri D, Strassburger K, Schikowski T, Ranft U . Traffic-related air pollution and incident type 2 diabetes: results from the SALIA cohort study. Environ Health Perspect. 2010; 118(9):1273-9. PMC: 2944089. DOI: 10.1289/ehp.0901689. View