Exploring the Advancements of AI Enabled Clinical Decision Support Systems for Patient Triage in Healthcare

As the demand for medical services grows, the need for an efficient care process becomes increasingly urgent. Recent events have highlighted that the demad for services exceeds the available supply. In response, the focus of heaht information technology has shifted toward advanced clinical decision support systems (CDSS) to addess the critical challenges facing the medical industry. However, many healthcare organiztions lack robust clinical systems and tools. This research presents a comprehensive study of current industry applications, its related works, and provides insights into optimizing current state of the art machine learning (ML) CDSS with newer technologies, such as generative artificial intelligence-based knowledge graphs. This paper offers an overview of CDSS, details the patient treatment process and triage methods, explores the capabilities of knowledge graphs in healthcare, and underscores the need to incorporate machine learning into these processes to increase intelligence, robustness, and personalization.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/healthcom60970.2024.10880833
Citations
2
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
1.42

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article

Exploring the Advancements of AI Enabled Clinical Decision Support Systems for Patient Triage in Healthcare

Jose M. Ruiz Mejia, Danda B. Rawat
2 citations
Artificial Intelligence in Healthcare
1.42
article

Exploring the Advancements of AI Enabled Clinical Decision Support Systems for Patient Triage in Healthcare

Jose M. Ruiz Mejia, Danda B. Rawat
article en
2 citations

Abstract

As the demand for medical services grows, the need for an efficient care process becomes increasingly urgent. Recent events have highlighted that the demad for services exceeds the available supply. In response, the focus of heaht information technology has shifted toward advanced clinical decision support systems (CDSS) to addess the critical challenges facing the medical industry. However, many healthcare organiztions lack robust clinical systems and tools. This research presents a comprehensive study of current industry applications, its related works, and provides insights into optimizing current state of the art machine learning (ML) CDSS with newer technologies, such as generative artificial intelligence-based knowledge graphs. This paper offers an overview of CDSS, details the patient treatment process and triage methods, explores the capabilities of knowledge graphs in healthcare, and underscores the need to incorporate machine learning into these processes to increase intelligence, robustness, and personalization.

Howard University (US)
Howard University
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare
1.42
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Exploring the Advancements of AI Enabled Clinical Decision Support Systems for Patient Triage in Healthcare — Jose M. Ruiz Mejia, Danda B. Rawat · (2024) | TGRS Research Map | TGRS