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Karamarie Fecho
University of North Carolina at Chapel Hill
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6704-9306
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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 have 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, 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.
Keywords
data integration, spatiotemporal data, modular software design
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CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 3
Posted 17 Feb, 2020
No community comments so far
Editorial decision: Accept
On 16 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
No comments provided
Editorial decision: Minor revision
On 12 Feb, 2020
Review #1 received
Received 08 Feb, 2020
Reviewers invited
Invitations sent on 06 Feb, 2020
Reviewer #1 agreed
On 06 Feb, 2020
Editor assigned
On 06 Feb, 2020
Submission checks complete
On 05 Feb, 2020
Editor invited
On 05 Feb, 2020
No comments provided
Editorial decision: Minor revision
On 17 Jan, 2020
Review #2 received
Received 16 Jan, 2020
Review #1 received
Received 07 Jan, 2020
Reviewer #2 agreed
On 27 Dec, 2019
Reviewers invited
Invitations sent on 24 Dec, 2019
Reviewer #1 agreed
On 24 Dec, 2019
Editor assigned
On 23 Dec, 2019
Submission checks complete
On 22 Dec, 2019
Editor invited
On 22 Dec, 2019
First submitted
On 19 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
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Subject Areas
Medical Informatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Software
Karamarie Fecho
University of North Carolina at Chapel Hill
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6704-9306
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 3
Posted 17 Feb, 2020
No community comments so far
Editorial decision: Accept
On 16 Feb, 2020
Editor assigned
On 14 Feb, 2020
Submission checks complete
On 13 Feb, 2020
Editor invited
On 13 Feb, 2020
No comments provided
Editorial decision: Minor revision
On 12 Feb, 2020
Review #1 received
Received 08 Feb, 2020
Reviewers invited
Invitations sent on 06 Feb, 2020
Reviewer #1 agreed
On 06 Feb, 2020
Editor assigned
On 06 Feb, 2020
Submission checks complete
On 05 Feb, 2020
Editor invited
On 05 Feb, 2020
No comments provided
Editorial decision: Minor revision
On 17 Jan, 2020
Review #2 received
Received 16 Jan, 2020
Review #1 received
Received 07 Jan, 2020
Reviewer #2 agreed
On 27 Dec, 2019
Reviewers invited
Invitations sent on 24 Dec, 2019
Reviewer #1 agreed
On 24 Dec, 2019
Editor assigned
On 23 Dec, 2019
Submission checks complete
On 22 Dec, 2019
Editor invited
On 22 Dec, 2019
First submitted
On 19 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
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 have 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, 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.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
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