Optimizing Healthcare Clinical Decision Support Systems: a Systems Thinking and Process Modeling Approach

Clinical Decision Support Systems (CDSS) are vital for enhancing diagnostic accuracy, treatment efficacy, and patient outcomes in modern healthcare. This paper adopts a systems thinking approach to examine and optimize Clinical Decision Support Systems (CDSS) within the broader context of healthcare delivery. While CDSS has been widely studied from technical and clinical perspectives, this work focuses on understanding the clinical decision-making (CDM) process itself as an emergent outcome shaped by a sequence of interrelated processes and actors. Using frameworks such as CATWOE, Idealized Design, and the Data-Information-Knowledge (DIK) model, the paper unveils the dual nature of CDSS-where both human expertise and machine intelligence collaboratively transform data into actionable knowledge. It identifies key shaping forces, stakeholders, and their expectations, and emphasizes the need for alignment with regulatory, ethical, and systemic considerations. A preliminary agent-based model is introduced to illustrate interdependencies among actors, setting the stage for future simulation-based evaluations. This integrated view provides actionable insights to inform more coherent, scalable, and contextaware CDSS implementations.