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

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Publicado en:Journal of Global Information Management vol. 33, no. 1 (2025), p. 1-18
Autor principal: Goh, Tiong T.
Otros Autores: Jiang, Philip Hong Wei, Wang, William Yu Chung, Hsieh, Chih-Chia
Publicado:
IGI Global
Materias:
Practitioner patient relationship
Information systems
Physicians
Machine learning
Research design
Decision support systems
Research methodology
Efficiency
Support networks
Hospitals
Knowledge
Emergency services
Architecture
Patients
Algorithms
Emergency medical care
Data processing
Accuracy
Diabetes
Emergency medical services
Health services
Segmentation
Decision making
Clinical outcomes
Electronic health records
Health care
Medical decision making
Artificial intelligence
Clinical decision making
Information technology
Design
Information management
Social
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1062-7375
DOI:10.4018/JGIM.371436
Fuente:ABI/INFORM Global