ArticleJMIR medical informatics2023
A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.
Article in JMIR medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence for Integrated Analysis of Non-Blood Biological Fluids: From Biomarker Discovery to Clinical Decision-Support Systems.Diagnostics (Basel, Switzerland) · 2026Review
- Process Improvement Before Artificial Intelligence and Automation: Building Trust With the Understand-Transform-Sustain Framework.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Artificial intelligence and synthetic biology in traditional Chinese medicine: revolutionizing public health applications.Frontiers in plant science · 2026Review
- Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.Frontiers in surgery · 2026Review
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.European journal of medical research · 2025Review
- Optimizing the efficiency and effectiveness of data quality assurance in a multicenter clinical dataset.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)-Based Data Model and Structure Implementations: Systematic Scoping Review.JMIR medical informatics · 2024Article
- Emerging Technologies in Healthcare and Laboratory Medicine: Trends and Need for a Roadmap to Sustainable ImplementationBalkan medical journal · 2024Article
- A scalable and transparent data pipeline for AI-enabled health data ecosystems.Frontiers in medicine · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
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Authors and funding
16 authors.
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Abstract
backgroundIncreasing 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.
objectiveIn this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.
methodsWe validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.
resultsWe 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.
conclusionsOur 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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