Work

VarDiff: A Conceptual Model for Representing Variable Differences Between Clinical Decision Support Systems

Year: 2026

Type: article

Abstract: Despite significant advancements in Artificial Intelligence, its widespread adoption in the clinical domain remains restricted due to the inherent complexity, fragmented nature, and diversity of healthcare systems. Each healthcare provider has unique data, clinical guidelines, data availability, system architectures, heterogeneity, and distribution. These challenges hinder the application of Clinical Decision Support Systems because of a limited understanding of how existing systems can be effectively redeployed across different healthcare providers. Redeployment is needed because it enables the reuse of existing knowledge, maximizes reusability, and avoids code duplication, thereby reducing the costs, effort, and time required to develop the Clinical Decision Support System from scratch. In addition, it ensures faster deployment and wider accessibility in the case of resource-constrained healthcare providers. An essential for redeployment is to identify the possible situations in which variables differ between two dynamic environments. To address this gap, we propose a structured multi-dimensional framework that systematically analyzes the potential differences between the variables. To represent the output of differences across dimensions based on variables in a systematic, machine-readable manner, we proposed a conceptual model, “VarDiff”, and a decision matrix of possible outcomes across five differential dimensions. This conceptual model provides a systematic, structural, and logical representation of a multidimensional framework for identifying differences among variables across data ecosystems. It formalizes variable characteristics in terms of semantic entities to observe differences among variables. The adaptation categories help identify the specific adaptation type, enabling the selection of relevant adaptation strategies in the “Mutator” component.

Source: Applied Sciences

DOI: 10.3390/app16073331

Authors: Gourav Gupta, Jan Stanek, Wolfgang Mayer, Georg Grossmann, Markus Stumptner

Institutions: Adelaide University, The University of Adelaide

Language: English

Topic: Electronic Health Records Systems

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896