From research to clinic: Accelerating the translation of clinical decision support systems by making synthetic data interoperable

Abstract

The translation of clinical decision support system (CDSS) tools from research settings into the clinic is often non-existent, partly because the focus tends to be on training machine learning models rather than tool development using the model for inference. To develop a CDSS tool that can be deployed in the clinical workflow, there is a need to integrate, validate, and test the tool on the Electronic Health Record (EHR) systems that store and manage patient data. Not surprisingly, it is rarely possible for researchers to get the necessary access to an EHR system due to legal restrictions pertaining to the protection of data privacy in patient records. We propose an architecture for using synthetic data in EHR systems to make CDSS tool development and testing much easier. In this study, the architecture is implemented in the SyntHIR system. SyntHIR has three noteworthy architectural features enabling (i) integration with synthetic data generators, (ii) data interoperability, and (iii) tool transportability. The translational value of this approach was evaluated through two primary steps. First, a working proof-of-concept of a machine learning-based CDSS tool was developed using data from patient registries in Norway. Second, the transportability of this CDSS tool was demonstrated by successfully deploying it in Norway's largest EHR system vendor (DIPS). These findings showcase the value of the SyntHIR architecture as a useful reference model to accelerate the translation of "bench to bedside" research of CDSS tools.