Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module
Cite this dataset
van Damme, Philip et al. (2024). Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module [Dataset]. Dryad. https://doi.org/10.5061/dryad.1jwstqk10
Abstract
Objective
To assess the use of Health Level Seven Fast Healthcare Interoperability Resources (FHIR®) for implementing the Findable, Accessible, Interoperable, and Reusable guiding principles for scientific data (FAIR). Additionally, present a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR.
Material and Methods
A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department dataset (MIMIC-ED), a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators.
Results
The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified.
Discussion
Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR.
Conclusion
To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures. Keywords: FAIR Guiding Principles, HL7 FHIR, Reusable Data, MIMIC-IV
README: FAIR Indicator Scores and Qualitative Comments
This dataset belongs as supplementary material to the paper entitled "Assessing the Use of HL7 FHIR for Implementing the FAIR Guiding Principles: A Case Study of the MIMIC-IV Emergency Department Module".
Description of the data and file structure
This dataset describes the indicator scores and qualitative comments of the FAIR data assessment of the Medical Information Mart for Intensive Care (MIMIC)-IV Emergency Department Module. Two distributions of the Emergency Department module were assessed, the PhysioNet distribution and the Fast Healthcare Interoperability Resources (FHIR) distribution. This dataset consists of two files: (1) PhysioNet.csv containing the data of the PhysioNet distribution; and (2) FHIR.csv containing the data of the FHIR distribution. Both files share the same structure and fields.
- Indicator ID: an ID corresponding to the IDs listed in Table 1 of the paper, which refer to a Research Data Alliance FAIR Data Maturity Model indicator.
- Score Rater A: the indicator score given by rater A after the assessment (binary, 0 or 1).
- Motivation Rater A: a textual motivation for the score given by rater A.
- Score Rater B: the indicator score given by rater B after the assessment (binary, 0 or 1).
- Motivation Rater B: a textual motivation for the score given by rater B.
- Motivation Rater C: a textual motivation for the final score given by rater C (only if there was disagreement between rater A and B).
- Final Score: the final indicator score, based on agreement between rater A and B, or in case of disagreement, the score of rater C.
Note on missing values (Motivation fields):
- If a rater scored an indicator and provided no motivation, the Motivation value is "No motivation provided".
- If a rater did not score an indicator and, therefore, provided no motivation, the Motivation value is "n/a".
Sharing/Access information
Links to other publicly accessible locations of the data:
- The data is not available elsewhere
Data was derived from the following sources:
- The data was generated during experiments and not derived from other sources
Methods
The authors of the paper collected the dataset.
Usage notes
Microsoft Word (.docx files) or Microsoft Excel (.csv files)
(Open-source alternatives: LibreOffice, OpenOffice)
The data files (.csv) can also be opened using any text editor, R, etc.
Funding
European Research Council, Award: 824666