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Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore

OBJECTIVES: Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians’ hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and...

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Autores principales: Huang, Zhilian, George, Mithun Mohan, Tan, Yi-Roe, Natarajan, Karthiga, Devasagayam, Emily, Tay, Evonne, Manesh, Abi, Varghese, George M., Abraham, Ooriapadickal Cherian, Zachariah, Anand, Yap, Peiling, Lall, Dorothy, Chow, Angela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd. on behalf of International Society of Chemotherapy for Infection and Cancer 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684720/
https://www.ncbi.nlm.nih.gov/pubmed/37640155
http://dx.doi.org/10.1016/j.jgar.2023.08.016
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author Huang, Zhilian
George, Mithun Mohan
Tan, Yi-Roe
Natarajan, Karthiga
Devasagayam, Emily
Tay, Evonne
Manesh, Abi
Varghese, George M.
Abraham, Ooriapadickal Cherian
Zachariah, Anand
Yap, Peiling
Lall, Dorothy
Chow, Angela
author_facet Huang, Zhilian
George, Mithun Mohan
Tan, Yi-Roe
Natarajan, Karthiga
Devasagayam, Emily
Tay, Evonne
Manesh, Abi
Varghese, George M.
Abraham, Ooriapadickal Cherian
Zachariah, Anand
Yap, Peiling
Lall, Dorothy
Chow, Angela
author_sort Huang, Zhilian
collection PubMed
description OBJECTIVES: Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians’ hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and facilitators in accepting an AI-enabled CDSS for antibiotic prescribing. METHODS: We conducted in-depth interviews with physicians from the National Centre for Infectious Diseases (NCID), Singapore, and Christian Medical College Vellore (CMCV), India, between April and December 2022. Our semi-structured in-depth interview guides were anchored on Venkatesh's UTAUT model. We used clinical vignettes to illustrate the application of AI in clinical decision support for antibiotic prescribing and explore medico-legal concerns. RESULTS: Most NCID physicians felt that an AI-enabled CDSS could facilitate antibiotic prescribing, while most CMCV physicians were sceptical about the tool's utility. The hesitancy in adopting an AI-enabled CDSS stems from concerns about the lack of validated and successful examples, fear of losing autonomy and clinical skills, difficulty of use, and impediment in work efficiency. Physicians from both sites felt that a user-friendly interface, integration with workflow, transparency of output, a guiding medico-legal framework, and training and technical support would improve the uptake of an AI-enabled CDSS. CONCLUSION: In conclusion, the acceptance of AI-enabled CDSSs depends on the physician's confidence with the tool's recommendations, perceived ease of use, familiarity with AI, the organisation's digital culture and support, and the presence of medico-legal governance of AI. Progressive implementation and continuous feedback are essential to allay scepticism around the utility of AI-enabled CDSSs.
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spelling pubmed-106847202023-12-01 Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore Huang, Zhilian George, Mithun Mohan Tan, Yi-Roe Natarajan, Karthiga Devasagayam, Emily Tay, Evonne Manesh, Abi Varghese, George M. Abraham, Ooriapadickal Cherian Zachariah, Anand Yap, Peiling Lall, Dorothy Chow, Angela J Glob Antimicrob Resist Article OBJECTIVES: Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians’ hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and facilitators in accepting an AI-enabled CDSS for antibiotic prescribing. METHODS: We conducted in-depth interviews with physicians from the National Centre for Infectious Diseases (NCID), Singapore, and Christian Medical College Vellore (CMCV), India, between April and December 2022. Our semi-structured in-depth interview guides were anchored on Venkatesh's UTAUT model. We used clinical vignettes to illustrate the application of AI in clinical decision support for antibiotic prescribing and explore medico-legal concerns. RESULTS: Most NCID physicians felt that an AI-enabled CDSS could facilitate antibiotic prescribing, while most CMCV physicians were sceptical about the tool's utility. The hesitancy in adopting an AI-enabled CDSS stems from concerns about the lack of validated and successful examples, fear of losing autonomy and clinical skills, difficulty of use, and impediment in work efficiency. Physicians from both sites felt that a user-friendly interface, integration with workflow, transparency of output, a guiding medico-legal framework, and training and technical support would improve the uptake of an AI-enabled CDSS. CONCLUSION: In conclusion, the acceptance of AI-enabled CDSSs depends on the physician's confidence with the tool's recommendations, perceived ease of use, familiarity with AI, the organisation's digital culture and support, and the presence of medico-legal governance of AI. Progressive implementation and continuous feedback are essential to allay scepticism around the utility of AI-enabled CDSSs. Published by Elsevier Ltd. on behalf of International Society of Chemotherapy for Infection and Cancer 2023-12 /pmc/articles/PMC10684720/ /pubmed/37640155 http://dx.doi.org/10.1016/j.jgar.2023.08.016 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Zhilian
George, Mithun Mohan
Tan, Yi-Roe
Natarajan, Karthiga
Devasagayam, Emily
Tay, Evonne
Manesh, Abi
Varghese, George M.
Abraham, Ooriapadickal Cherian
Zachariah, Anand
Yap, Peiling
Lall, Dorothy
Chow, Angela
Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title_full Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title_fullStr Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title_full_unstemmed Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title_short Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore
title_sort are physicians ready for precision antibiotic prescribing? a qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in india and singapore
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684720/
https://www.ncbi.nlm.nih.gov/pubmed/37640155
http://dx.doi.org/10.1016/j.jgar.2023.08.016
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