TY  - JOUR
AU  - Williams, Elena
AU  - Kienast, Manuel
AU  - Medawar, Evelyn
AU  - Reinelt, Janis
AU  - Merola, Alberto
AU  - Klopfenstein, Sophie Anne Ines
AU  - Flint, Anne Rike
AU  - Heeren, Patrick
AU  - Poncette, Akira-Sebastian
AU  - Balzer, Felix
AU  - Beimes, Julian
AU  - von Bünau, Paul
AU  - Chromik, Jonas
AU  - Arnrich, Bert
AU  - Scherf, Nico
AU  - Niehaus, Sebastian
PY  - 2023
DA  - 2023/3/21
TI  - A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study
JO  - JMIR Med Inform
SP  - e43847
VL  - 11
KW  - data interoperability
KW  - fast healthcare interoperability resources
KW  - FHIR
KW  - data standardization pipeline
KW  - medical information mart for intensive care
KW  - MIMIC IV
KW  - artificial intelligence
KW  - AI application
KW  - AI
KW  - deployment
KW  - data
KW  - usability
KW  - care unit
KW  - diagnosis
KW  - cooperation
KW  - patient care
KW  - care
KW  - medical research
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2023/1/e43847
UR  - https://doi.org/10.2196/43847
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36943344
DO  - 10.2196/43847
ID  - info:doi/10.2196/43847
ER  -