Medical Process Modeling with a Hybrid System Dynamics Zachman Framework

Abstract

Diagnoses are perhaps the most complex and crucial decisions within the modern healthcare enterprise. Models for complex healthcare decisions must incorporate consideration for the usual multiplicity of important factors, interacting feedback loops among these factors, and the dynamic nature of the full diagnostic arena. A diagnoses modeling technique that has the requisite variety of relevant considerations is presented. The technique has the potential to overcome mandatory time criteria, while considering the competence and robustness of high importance diagnostic decisions. In this study, descriptive narratives dictated by examining physicians who were directly involved in the diagnosis and treatment of patients were examined in detail not only to extract key factors involved in medical decision making processes, but also to illustrate the wide ontological origin of key decision making factors. Important factors in the narratives were identified and mapped with a new System Dynamics methodology that incorporates a Zachman Framework for establishing the overall scope and context of the full medical decision making context within the modern medical enterprise. The two techniques produce a synergy that addresses the debilities of the techniques in isolation, allowing enhanced comprehension of diagnostic processes, and their improvement.