FHIR-DHP: A Standardized Clinical Data Harmonisation Pipeline for scalable AI application deployment

Background Increasing digitalisation in the medical domain gives rise to large amounts of healthcare 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 non-standardised data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the healthcare system. Despite the existence of standardised data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remains limited. Objective We developed a data harmonisation 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 MIMIC IV database including > 40,000 patients admitted to an intensive care unit. Results We present the FHIR-DHP workflow in respect of transformation of “raw” hospital records into a harmonised, AI-friendly data representation. The pipeline consists of five key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonised 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 scalable and needs-driven data modelling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step towards increasing cooperation, interoperability and quality of patient care in the clinical routine and for medical research. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was partially funded by the German Federal Ministry of Education and Research under Grant 16SV8559. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: MIMIC IV database which was used in this study is openly available to credentialed users who sign 'Data Use Agreement' at PhysioNet website (20). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes MIMIC IV database which was used in this study is openly available to credentialed users who sign 'Data Use Agreement' at PhysioNet website (20). The code is not publicly available due to privacy but a demo is available from the corresponding author on request. * Abbreviation : Full name DHP : Data Harmonisation Pipeline EHR : Electronic Health Record FHIR : Fast Healthcare Interoperability Resources JSON : JavaScript Object Notation MIMIC : Medical Information Mart for Intensive Care RDF : Resource Description Format XML : Extensible Markup Language