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

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Highlights

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Singapore physicians were mostly accepting of AI tools for antibiotic prescribing.
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India physicians were skeptical about the utility and value of AI tools.
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Practice context and digital culture shape physicians’ acceptance of AI tools.
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Validating AI tools will instill physicians’ confidence in adopting them.
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Medico-legal oversight of AI may dispel physicians’ hesitancy in adopting AI tools.

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.

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Most cited references 29

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User Acceptance of Information Technology: Toward a Unified View

Davis, Venkatesh, Morris (2003)

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Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

(2022)

Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

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An overview of clinical decision support systems: benefits, risks, and strategies for success

Reed Sutton, David Pincock, Daniel Baumgart … (2020)

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

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Author and article information

Contributors

Dorothy Lall

Angela Chow

Journal

Journal ID (nlm-ta): J Glob Antimicrob Resist

Journal ID (iso-abbrev): J Glob Antimicrob Resist

Title: Journal of Global Antimicrobial Resistance

Publisher: Published by Elsevier Ltd. on behalf of International Society of Chemotherapy for Infection and Cancer

ISSN (Print): 2213-7165

ISSN (Electronic): 2213-7173

Publication date PMC-release: 1 December 2023

Publication date (Print): December 2023

Volume: 35

Pages: 76-85

Affiliations

[a ]Infectious Diseases Research and Training Office, National Centre for Infectious Diseases, Singapore

[b ]Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge [OCEAN], Tan Tock Seng Hospital, Singapore

[c ]Department of Infectious Diseases, Christian Medical College, Vellore, Tamil Nadu, India

[d ]International Digital Health & AI Research Collaborative (I-DAIR), Geneva, Switzerland

[e ]Department of Medicine, Christian Medical College, Vellore, Tamil Nadu, India

[f ]Department of Community Health, Christian Medical College Vellore - Chittoor Campus, Andhra Pradesh, India

[g ]Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore

[h ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore

Author notes

[* ]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. Angela_Chow@ 123456ttsh.com.sg

[** ]Alternative corresponding author. Mailing address: Department of Community Health, Christian Medical College Vellore - Chittoor Campus, Andhra Pradesh, India 517002. dorothy.lall@ 123456cmcvellore.ac.in

[1]

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Article

Publisher Item ID: S2213-7165(23)00140-6

DOI: 10.1016/j.jgar.2023.08.016

PMC ID: 10684720

PubMed ID: 37640155

SO-VID: 7c8111f2-f964-4f6c-bf2e-523a25ca9411

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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

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Highlights

Abstract

Objectives

Methods

Results

Conclusion

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Artificial Intelligence in Medicine

Most cited references 29

User Acceptance of Information Technology: Toward a Unified View

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

An overview of clinical decision support systems: benefits, risks, and strategies for success

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