%0 Journal Article %1 Williams2023-rz %A Williams, Elena %A Kienast, Manuel %A Medawar, Evelyn %A Reinelt, Janis %A Merola, Alberto %A Klopfenstein, Sophie Anne Ines %A Flint, Anne Rike %A Heeren, Patrick %A Poncette, Akira-Sebastian %A Balzer, Felix %A Beimes, Julian %A von Bünau, Paul %A Chromik, Jonas %A Arnrich, Bert %A Scherf, Nico %A Niehaus, Sebastian %D 2023 %I JMIR Publications Inc. %J JMIR Med. Inform. %K AI AI; FHIR; IV; MIMIC application; artificial care care; cooperation; data data; deployment; diagnosis; fast for healthcare information intelligence; intensive interoperability interoperability; mart medical patient pipeline; research; resources; standardization unit; usability %P e43847 %T A standardized clinical data harmonization pipeline for scalable AI application deployment (FHIR-DHP): Validation and usability study %V 11 %X 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.

@article{Williams2023-rz, 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.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Williams, Elena and Kienast, Manuel and Medawar, Evelyn and Reinelt, Janis and Merola, Alberto and Klopfenstein, Sophie Anne Ines and Flint, Anne Rike and Heeren, Patrick and Poncette, Akira-Sebastian and Balzer, Felix and Beimes, Julian and von B{\"u}nau, Paul and Chromik, Jonas and Arnrich, Bert and Scherf, Nico and Niehaus, Sebastian}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2fdde60203018ec523703c789f89c119a/scadsfct}, interhash = {160a98af63ec5191da45aad1a9e71bd7}, intrahash = {fdde60203018ec523703c789f89c119a}, journal = {JMIR Med. Inform.}, keywords = {AI AI; FHIR; IV; MIMIC application; artificial care care; cooperation; data data; deployment; diagnosis; fast for healthcare information intelligence; intensive interoperability interoperability; mart medical patient pipeline; research; resources; standardization unit; usability}, language = {en}, month = mar, pages = {e43847}, publisher = {JMIR Publications Inc.}, timestamp = {2024-09-10T10:45:25.000+0200}, title = {A standardized clinical data harmonization pipeline for scalable {AI} application deployment ({FHIR-DHP)}: Validation and usability study}, volume = 11, year = 2023 }