Designing a System Integration Architecture for a Machine Learning-Based Clinical Decision Support System in the Emergency Department Setting: A Design Science Approach

Abstract

Physicians working in emergency departments (ED) face significant challenges due to inadequate clinical decision support and the fragmentation of hospital information systems (HIS). This study develops a system integration architecture that facilitates the creation of a clinical decision support system (CDSS) based on machine learning to improve decision-making in the ED. Design science research methodology was employed to create and evaluate the designed architecture which provides integration of HIS systems within the ED setting. The research enables seamless accessing and processing of data from disparate HIS systems, allowing the implementation of machine learning techniques for enhanced clinical decision support. The designed system integration architecture enables the CDSS and allows physicians to address patients' data more effectively, leading to better-informed clinical decisions and significantly enhancing decision support capabilities in the ED, contributing to improved healthcare outcomes and hospital expenditure by leveraging machine learning techniques.