Model-based medical decision making: From system dynamics to object-process methodology

Abstract

Decision-making in mission-critical domains, including healthcare and finance, requires information structures that are explainable, trustworthy, and semantically coherent. This study examines how hybrid conceptual modeling addresses these needs by transforming a System Dynamics (SD) model into an Object-Process Methodology (OPM) ISO 19,450 model, supporting transparent, stakeholder-aligned decision-making. Using an established pandemic response scenario, we introduce a five-phase decision support system development framework, within which we propose a systematic SD-to-OPM transformation algorithm that explicates causal relationships, exposes implicit assumptions, and improves knowledge transparency. An empirical validation with 50 healthcare professionals across three expertise levels assessed perceptions of model clarity, interpretability, and decision-support utility. Wilcoxon signed-rank tests and bootstrap confidence intervals showed statistically significant, large effect sizes (r > .50), with participants rating the OPM model above the SD model on all attributes examined. The confirmation of all three intended transformation effects supports constructing new AI-enhanced information systems for mission-critical domains.