Technology Forecast of Artificial Intelligence Applied to Medication Decision Support System

This study analyzes the decision-making support system based on artificial intelligence, which provides decision-making support and suggestions for doctors' clinical medication, and its technical characteristics. The study conducted interviews with 6 experts and scholars and analyzed a total of 267 experts with questionnaires. Three rounds of questionnaire interviews and responses were conducted from the four dimensions of “technology”, “process”, “behavior” and “influence”, and a mixed method approach was adopted. The analysis method is used to analyze to clarify the technical predictions or expectations of technology developers and technology users for the application of artificial intelligence in clinical medication decision support systems. The research results indicate that the construction of the system requires a large amount of patient medical information, clinical treatment guidelines and other medical professional knowledge for technology developers. For technology users, there are issues related to “user field” and “user habits” regarding user processes and behaviors, so opinions are inconsistent. In particular, clinical experts believe that critical procedures and high-risk situations will affect the utilization rate of artificial intelligence introduction. Therefore, system development should include medical personnel to provide suggestions and feedback on the construction of the system, in terms of medical information, clinical treatment guidelines and other medical professional knowledge.

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Published
2024-08-04
DOI
https://doi.org/10.23919/picmet64035.2024.10653223
Primary Topic
Artificial Intelligence in Healthcare
Type
article
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Technology Forecast of Artificial Intelligence Applied to Medication Decision Support System

Kuo‐Liang Chen, Kae‐Kuen Hu, Chen Peng, Chia‐Min Lin et al.
Artificial Intelligence in Healthcare
article

Technology Forecast of Artificial Intelligence Applied to Medication Decision Support System

Kuo‐Liang Chen, Kae‐Kuen Hu, Chen Peng, Chia‐Min Lin, Mo-An Chu, Li-Ling Cho
article en

Abstract

This study analyzes the decision-making support system based on artificial intelligence, which provides decision-making support and suggestions for doctors' clinical medication, and its technical characteristics. The study conducted interviews with 6 experts and scholars and analyzed a total of 267 experts with questionnaires. Three rounds of questionnaire interviews and responses were conducted from the four dimensions of “technology”, “process”, “behavior” and “influence”, and a mixed method approach was adopted. The analysis method is used to analyze to clarify the technical predictions or expectations of technology developers and technology users for the application of artificial intelligence in clinical medication decision support systems. The research results indicate that the construction of the system requires a large amount of patient medical information, clinical treatment guidelines and other medical professional knowledge for technology developers. For technology users, there are issues related to “user field” and “user habits” regarding user processes and behaviors, so opinions are inconsistent. In particular, clinical experts believe that critical procedures and high-risk situations will affect the utilization rate of artificial intelligence introduction. Therefore, system development should include medical personnel to provide suggestions and feedback on the construction of the system, in terms of medical information, clinical treatment guidelines and other medical professional knowledge.

National Taiwan University of Science and Technology (TW), National Taiwan University (TW), National Kaohsiung University of Science and Technology (TW)
Peace, Justice and strong institutions
Openalex Percentile: Top 81%
Artificial Intelligence in Healthcare
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Technology Forecast of Artificial Intelligence Applied to Medication Decision Support System — Kuo‐Liang Chen, Kae‐Kuen Hu, et al. · (2024) | TGRS Research Map | TGRS