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Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey.

作者信息

Zheng Rui, Jiang Xiao, Shen Li, He Tianrui, Ji Mengting, Li Xingyi, Yu Guangjun

机构信息

Shanghai Children's Hospital, Shanghai, China.

School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

出版信息

J Med Internet Res. 2025 Apr 7;27:e62732. doi: 10.2196/62732.


DOI:10.2196/62732
PMID:40194276
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12012391/
Abstract

BACKGROUND: An intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice. OBJECTIVE: The aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intentions to adopt it and by putting forward targeted management recommendations. METHODS: This study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of "task-technology fit" and "perceived ease of use" on clinicians' intentions to use the CDSS when mediated by "performance expectation" and "perceived risk." We collated and analyzed the responses to the open-ended question. RESULTS: We collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=-0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=-0.281; P<.001) and perceived ease of use (β=-0.377; P<.001) negatively affected perceived risk. Perceived risk (β=-0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians' perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration. CONCLUSIONS: Perceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d631/12012391/45478f6b7d6f/jmir_v27i1e62732_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d631/12012391/77692e5dec12/jmir_v27i1e62732_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d631/12012391/45478f6b7d6f/jmir_v27i1e62732_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d631/12012391/77692e5dec12/jmir_v27i1e62732_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d631/12012391/45478f6b7d6f/jmir_v27i1e62732_fig2.jpg

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Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey.

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本文引用的文献

[1]
Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI).

Langenbecks Arch Surg. 2025-1-28

[2]
Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

JMIR Form Res. 2024-4-16

[3]
Development and Pilot Implementation of Neotree, a Digital Quality Improvement Tool Designed to Improve Newborn Care and Survival in 3 Hospitals in Malawi and Zimbabwe: Cost Analysis Study.

JMIR Mhealth Uhealth. 2023-12-22

[4]
Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review.

J Med Internet Res. 2023-12-8

[5]
Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

J Healthc Eng. 2023

[6]
Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey.

J Med Internet Res. 2022-11-7

[7]
Factors influencing clinicians' willingness to use an AI-based clinical decision support system.

Front Digit Health. 2022-8-16

[8]
Experimental evidence of effective human-AI collaboration in medical decision-making.

Sci Rep. 2022-9-2

[9]
When Does Da Vanci Robotic Surgical Systems Come Into Play?

Front Public Health. 2022

[10]
Reaching 95%: decision support tools are the surest way to improve diagnosis now.

BMJ Qual Saf. 2022-6

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