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.

翻译：利用高质量的患者病历和健康登记数据开发基于机器学习的临床决策支持系统（CDSS）具有巨大潜力。在临床工作流程中应用CDSS工具时，需要在使用电子健康记录（EHR）系统存储和管理患者数据的基础上，对该工具进行集成、验证和测试。然而，由于法律合规要求，往往无法获得对EHR系统的必要访问权限。本文提出了一种用于生成和使用综合EHR数据以支持CDSS工具开发的架构，该架构在名为SyntHIR的系统中实现。SyntHIR系统采用快速医疗互操作性资源（FHIR）标准实现数据互操作性，使用Gretel框架生成综合数据，以微软Azure FHIR服务器作为基于FHIR的EHR系统，并利用SMART on FHIR框架实现工具可移植性。我们通过使用挪威患者登记（NPR）和挪威患者处方（NorPD）数据开发基于机器学习的CDSS工具，证明了SyntHIR的实用性。我们在SyntHIR系统上展示了该工具的开发过程，并将其移植到Open DIPS环境中。结论表明，SyntHIR为利用综合FHIR数据开发CDSS工具提供了通用架构，并提供了在临床部署前进行测试的环境。然而，生成综合数据的质量仍有改进空间。代码已开源，可从https://github.com/potter-coder89/SyntHIR.git获取。