Design Principles for Machine Learning Based Clinical Decision Support Systems: A Design Science Study
摘要
Employing a design science research approach building on four modes of inquiry, this study presents a Clinical Decision Support System for predicting heart failure readmissions, combining machine learning, inpatient care process analysis, and user experience design. It introduces three key design principles: contextual integration, actionable insights, and adaptive explanation levels, to support the design of decision support in clinical settings. The research, while focused on a specific healthcare context, offers a model for integrating technical precision and user-centric design in inpatient care processes, suggesting broader applications and future research directions in diverse healthcare environments.