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
ABSTRACT: 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 ba...
| Κύριοι συγγραφείς: | , , , , , , , , , , , , |
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| Μορφή: | Άρθρο |
| Γλώσσα: | English |
| Έκδοση: |
Elsevier
2023-12-01
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| Σειρά: | Journal of Global Antimicrobial Resistance |
| Θέματα: | |
| Διαθέσιμο Online: | http://www.sciencedirect.com/science/article/pii/S2213716523001406 |
| _version_ | 1827630975223332864 |
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| author | Zhilian Huang Mithun Mohan George Yi-Roe Tan Karthiga Natarajan Emily Devasagayam Evonne Tay Abi Manesh George M. Varghese Ooriapadickal Cherian Abraham Anand Zachariah Peiling Yap Dorothy Lall Angela Chow |
| author_facet | Zhilian Huang Mithun Mohan George Yi-Roe Tan Karthiga Natarajan Emily Devasagayam Evonne Tay Abi Manesh George M. Varghese Ooriapadickal Cherian Abraham Anand Zachariah Peiling Yap Dorothy Lall Angela Chow |
| author_sort | Zhilian Huang |
| collection | DOAJ |
| description | ABSTRACT: 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. |
| first_indexed | 2024-03-09T14:14:33Z |
| format | Article |
| id | doaj.art-3827b8f1a80f412cb85ba4b0799e77cf |
| institution | Directory Open Access Journal |
| issn | 2213-7165 |
| language | English |
| last_indexed | 2024-03-09T14:14:33Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Global Antimicrobial Resistance |
| spelling | doaj.art-3827b8f1a80f412cb85ba4b0799e77cf2023-11-29T04:24:23ZengElsevierJournal of Global Antimicrobial Resistance2213-71652023-12-01357685Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and SingaporeZhilian Huang0Mithun Mohan George1Yi-Roe Tan2Karthiga Natarajan3Emily Devasagayam4Evonne Tay5Abi Manesh6George M. Varghese7Ooriapadickal Cherian Abraham8Anand Zachariah9Peiling Yap10Dorothy Lall11Angela Chow12Infectious Diseases Research and Training Office, National Centre for Infectious Diseases, Singapore; Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, SingaporeDepartment of Infectious Diseases, Christian Medical College, Vellore, Tamil Nadu, IndiaInternational Digital Health & AI Research Collaborative (I-DAIR), Geneva, SwitzerlandInfectious Diseases Research and Training Office, National Centre for Infectious Diseases, Singapore; Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, SingaporeDepartment of Infectious Diseases, Christian Medical College, Vellore, Tamil Nadu, IndiaInfectious Diseases Research and Training Office, National Centre for Infectious Diseases, Singapore; Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, SingaporeDepartment of Infectious Diseases, Christian Medical College, Vellore, Tamil Nadu, IndiaDepartment of Infectious Diseases, Christian Medical College, Vellore, Tamil Nadu, IndiaDepartment of Medicine, Christian Medical College, Vellore, Tamil Nadu, IndiaDepartment of Medicine, Christian Medical College, Vellore, Tamil Nadu, IndiaInternational Digital Health & AI Research Collaborative (I-DAIR), Geneva, SwitzerlandDepartment of Community Health, Christian Medical College Vellore - Chittoor Campus, Andhra Pradesh, India; Alternative corresponding author. Mailing address: Department of Community Health, Christian Medical College Vellore - Chittoor Campus, Andhra Pradesh, India 517002.Infectious Diseases Research and Training Office, National Centre for Infectious Diseases, Singapore; Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Corresponding author. Mailing address: Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore 308433.ABSTRACT: 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.http://www.sciencedirect.com/science/article/pii/S2213716523001406Antimicrobial resistanceArtificial intelligenceClinical decision support systemAntibiotic prescribing |
| spellingShingle | Zhilian Huang Mithun Mohan George Yi-Roe Tan Karthiga Natarajan Emily Devasagayam Evonne Tay Abi Manesh George M. Varghese Ooriapadickal Cherian Abraham Anand Zachariah Peiling Yap Dorothy Lall Angela Chow 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 Journal of Global Antimicrobial Resistance Antimicrobial resistance Artificial intelligence Clinical decision support system Antibiotic prescribing |
| 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 | Antimicrobial resistance Artificial intelligence Clinical decision support system Antibiotic prescribing |
| url | http://www.sciencedirect.com/science/article/pii/S2213716523001406 |
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