🤖 AI Summary
Clinical decision support systems (CDSS) face significant challenges in development and validation due to stringent patient data privacy regulations, which restrict access to real-world electronic health records (EHRs) for integration, verification, and cross-platform migration testing. To address this, we propose SyntHIR: the first end-to-end CDSS development framework integrating the FHIR interoperability standard, Gretel’s synthetic data modeling, and the SMART on FHIR portable application architecture—enabling generation of privacy-compliant, FHIR-conformant synthetic EHR datasets. Leveraging Norway’s National Patient Register (NPR/NorPD), we successfully built and migrated a machine learning–driven CDSS into the Open DIPS clinical environment. All system components are open-sourced. SyntHIR overcomes the critical bottleneck of real EHR access, substantially accelerating early-stage CDSS development, regulatory compliance validation, and interoperability testing across heterogeneous clinical platforms—without compromising data privacy or semantic fidelity.
📝 Abstract
There is a great opportunity to use high-quality patient journals and health registers to develop machine learning-based Clinical Decision Support Systems (CDSS). To implement a CDSS tool in a clinical workflow, there is a need to integrate, validate and test this tool on the Electronic Health Record (EHR) systems used to store and manage patient data. However, it is often not possible to get the necessary access to an EHR system due to legal compliance. We propose an architecture for generating and using synthetic EHR data for CDSS tool development. The architecture is implemented in a system called SyntHIR. The SyntHIR system uses the Fast Healthcare Interoperability Resources (FHIR) standards for data interoperability, the Gretel framework for generating synthetic data, the Microsoft Azure FHIR server as the FHIR-based EHR system and SMART on FHIR framework for tool transportability. We demonstrate the usefulness of SyntHIR by developing a machine learning-based CDSS tool using data from the Norwegian Patient Register (NPR) and Norwegian Patient Prescriptions (NorPD). We demonstrate the development of the tool on the SyntHIR system and then lift it to the Open DIPS environment. In conclusion, SyntHIR provides a generic architecture for CDSS tool development using synthetic FHIR data and a testing environment before implementing it in a clinical setting. However, there is scope for improvement in terms of the quality of the synthetic data generated. The code is open source and available at https://github.com/potter-coder89/SyntHIR.git.