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. 2023 Mar 21:11:e43847.
doi: 10.2196/43847.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Elena Williams  1 , Manuel Kienast  1 , Evelyn Medawar  1 , Janis Reinelt  1 , Alberto Merola  1 , Sophie Anne Ines Klopfenstein  2 , Anne Rike Flint  2 , Patrick Heeren  2 , Akira-Sebastian Poncette  2 , Felix Balzer  2 , Julian Beimes  3 , Paul von Bünau  3 , Jonas Chromik  4 , Bert Arnrich  4 , Nico Scherf  5 , Sebastian Niehaus  1
Affiliations

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Elena Williams et al. JMIR Med Inform. .

Abstract

Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

Objective: In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.

Methods: We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.

Results: We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.

Conclusions: Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

Keywords: AI; AI application; FHIR; MIMIC IV; artificial intelligence; care; care unit; cooperation; data; data interoperability; data standardization pipeline; deployment; diagnosis; fast healthcare interoperability resources; medical information mart for intensive care; medical research; patient care; usability.

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Conflict of interest statement

Conflicts of Interest: FB reports grants from the German Federal Ministry of Education and Research, German Federal Ministry of Health, Berlin Institute of Health, personal fees from Elsevier Publishing, grants from Hans Böckler Foundation, other from Robert Koch Institute, grants from Einstein Foundation, grants from Berlin University Alliance, personal fees from Medtronic and personal fees from GE Healthcare.

Figures

Figure 1
Figure 1
Conceptual overview for an exemplary Fast Healthcare Interoperability Resources (FHIR) structure and hospital record, which are transformed from FHIR standard to an artificial intelligence (AI)–friendly format.
Figure 2
Figure 2
Flowchart showing an example diagnosis data being processed through the 5 stages in Fast Healthcare Interoperability Resources (FHIR) data harmonization pipeline (DHP). The first stage (a) includes querying of the diagnoses records, at the second stage (b-c) the data are mapped to FHIR standard, and the third stage carries out the syntactic resource validation. (f) If the FHIR resource is successfully validated, it is being transferred into the patient-model database (DB), and then (g) exported in a custom artificial intelligence (AI)–friendly JSON format.
See this image and copyright information in PMC

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