MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format

Author:

Bennett Alex M1ORCID,Ulrich Hannes2ORCID,van Damme Philip34ORCID,Wiedekopf Joshua5ORCID,Johnson Alistair E W1ORCID

Affiliation:

1. Child Health Evaluative Sciences, The Hospital for Sick Children , Toronto, Ontario, Canada

2. Institute for Medical Informatics and Statistics, Kiel University and University Hospital Center Schleswig-Holstein , Campus Kiel, Germany

3. Department of Medical Informatics, Amsterdam UMC Location University of Amsterdam , Amsterdam, The Netherlands

4. Amsterdam Public Health, Digital Health & Methodology , Amsterdam, The Netherlands

5. IT Center for Clinical Research, University of Lübeck and University Hospital Center Schleswig-Holstein , Campus Lübeck, Germany

Abstract

AbstractObjectiveConvert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR.Materials and MethodsFHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide.ResultsThe modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet.DiscussionOur work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work.ConclusionThe newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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