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  A standardized clinical data harmonization pipeline for scalable AI application deployment (FHIR-DHP): Validation and usability study

Williams, E., Kienast, M., Medawar, E., Reinelt, J., Merola, A., Klopfenstein, S. A. I., et al. (2023). A standardized clinical data harmonization pipeline for scalable AI application deployment (FHIR-DHP): Validation and usability study. JMIR Medical Informatics (JMI), 11: e43847. doi:10.2196/43847.

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 Creators:
Williams, Elena1, Author
Kienast, Manuel1, Author
Medawar, Evelyn1, Author
Reinelt, Janis1, Author
Merola, Alberto1, Author
Klopfenstein, Sophie Anne Ines2, Author
Flint, Anne Rike2, Author
Heeren, Patrick2, Author
Poncette, Akira-Sebastian2, Author
Balzer, Felix2, Author
Beimes, Julian3, Author
von Bünau, Paul3, Author
Chromik, Jonas4, Author
Arnrich, Bert4, Author
Scherf, Nico5, Author                 
Niehaus, Sebastian1, Author
Affiliations:
1AICURA Medical GmbH, Berlin, Germany, ou_persistent22              
2Institute of Medical Informatics, Charité University Medicine Berlin, Germany, ou_persistent22              
3idalab GmbH, Berlin, Germany, ou_persistent22              
4Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Germany, ou_persistent22              
5Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3282987              

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Free 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
 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.

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Language(s): eng - English
 Dates: 2023-01-242022-11-072023-01-252023-03-212023-03-21
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.2196/43847
PMID: 36943344
PMC: PMC10131740
 Degree: -

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Project name : -
Grant ID : 16SV8559
Funding program : -
Funding organization : Federal Ministry of Education and Research (BMBF)

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Title: JMIR Medical Informatics (JMI)
Source Genre: Journal
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Publ. Info: Toronto, ON, Canada : JMIR Publications
Pages: - Volume / Issue: 11 Sequence Number: e43847 Start / End Page: - Identifier: ISSN: 2291-9694
CoNE: https://pure.mpg.de/cone/journals/resource/2291-9694