Bridging Predictive Modelling and Healthcare Workflows: A Machine Learning-Enabled Clinical Decision Support System
Clinical decision support systems (CDSS) have demonstrated the potential to improve healthcare quality by delivering timely, patient-specific insights at the point of care; however, many machine learning (ML)–based approaches remain confined to research settings due to challenges in system integration, workflow alignment, and deployability. This paper presents the design and evaluation of a ML-enabled CDSS that bridges comparative predictive modelling with a modular system architecture suitable for clinical deployment. Multiple supervised ML models, including Random Forests, Logistic Regression, Support Vector Machines, Neural Networks, and XGBoost, were trained and evaluated to predict future serum potassium levels using routinely collected laboratory biomarkers. Model performance was assessed using regression-based metrics, including mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), and coefficient of determination (R^2). Evaluation using de-identified data from patients in British Columbia, Canada, demonstrated that the Random Trees model achieved the best overall regression performance (MAE = 0.28, RMSE = 0.37, R^2= 0.61, MAPE = 6.32). This model was selected for deployment within the proposed Predictive Risk Indicator for Serum Potassium Measurement (PRISM) clinical decision support system, enabling real-time generation of individualized predictions mapped to clinically meaningful risk categories. This work demonstrates the feasibility of integrating comparative ML model development with system-level integration through a model-as-a-service paradigm. The proposed system provides a foundation for future prospective validation, usability evaluation, and integration into routine clinical workflows.