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

Alex M Bennett; Hannes Ulrich; Philip van Damme; Joshua Wiedekopf; Alistair E W Johnson

doi:10.1093/jamia/ocad002

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

J Am Med Inform Assoc. 2023 Mar 16;30(4):718-725. doi: 10.1093/jamia/ocad002.

Authors

Alex M Bennett¹, Hannes Ulrich², Philip van Damme^{3

4}, Joshua Wiedekopf⁵, Alistair E W Johnson¹

Affiliations

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

Objective: Convert 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 methods: FHIR 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.

Results: The 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.

Discussion: Our 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.

Conclusion: The 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.

Keywords: HL7 FHIR; MIMIC-IV; electronic health records; fast healthcare interoperability resources; interoperability; open data.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Datasets as Topic
Electronic Health Records
Health Level Seven*
Humans
Information Dissemination* / methods
Information Storage and Retrieval* / methods
Information Storage and Retrieval* / standards
Patients*
Reproducibility of Results