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Cited by 66Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsTechnology Adoption and User Behaviour

Factors influencing clinicians' willingness to use an AI-based clinical decision support system

Open access
Choudhury, Avishek·2022·Frontiers in Digital Health
DOI 10.3389/fdgth.2022.920662PMC PMC9628998
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Abstract

Background: Given the opportunities created by artificial intelligence (AI) based decision support systems in healthcare, the vital question is whether clinicians are willing to use this technology as an integral part of clinical workflow. Purpose: This study leverages validated questions to formulate an online survey and consequently explore cognitive human factors influencing clinicians' intention to use an AI-based Blood Utilization Calculator (BUC), an AI system embedded in the electronic health record that delivers data-driven personalized recommendations for the number of packed red blood cells to transfuse for a given patient. Method: A purposeful sampling strategy was used to exclusively include BUC users who are clinicians in a university hospital in Wisconsin. We recruited 119 BUC users who completed the entire survey. We leveraged structural equation modeling to capture the direct and indirect effects of “AI Perception” and “Expectancy” on clinicians' Intention to use the technology when mediated by “Perceived Risk”. Results: The findings indicate a significant negative relationship concerning the direct impact of AI's perception on BUC Risk (ß = −0.23, p < 0.001). Similarly, Expectancy had a significant negative effect on Risk (ß = −0.49, p < 0.001). We also noted a significant negative impact of Risk on the Intent to use BUC (ß = −0.34, p < 0.001). Regarding the indirect effect of Expectancy on the Intent to Use BUC, the findings show a significant positive impact mediated by Risk (ß = 0.17, p = 0.004). The study noted a significant positive and indirect effect of AI Perception on the Intent to Use BUC when mediated by risk (ß = 0.08, p = 0.027). Overall, this study demonstrated the influences of expectancy, perceived risk, and perception of AI on clinicians' intent to use BUC (an AI system). AI developers need to emphasize the benefits of AI technology, ensure ease of use (effort expectancy), clarify the system's potential (performance expectancy), and minimize the risk perceptions by improving the overall design. Conclusion: Identifying the factors that determine clinicians' intent to use AI-based decision support systems can help improve technology adoption and use in the healthcare domain. Enhanced and safe adoption of AI can uplift the overall care process and help standardize clinical decisions and procedures. An improved AI adoption in healthcare will help clinicians share their everyday clinical workload and make critical decisions.

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Crossref · 10.3389/fdgth.2022.920662

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  • If an algorithm is not performing up to standards or adds more work to the clinicians, this can impact adoption, as clinicians perceive it as a high risk.

    “Finally, Choudhury [61] noted that if an algorithm is not performing up to standards or adds more work to the clinicians, this can impact adoption, as clinicians perceive it as a high risk.”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [61]

    The Discussion (page unavailable) reports that poorer effort and performance expectancy worsened perceived risk, and that greater perceived risk reduced clinicians’ intention to use the Blood Utilization Calculator. The Instrumentation section defines these expectancies as ease of use and perceived usefulness, but the paper does not specifically establish that an algorithm adding to clinicians’ workload causes them to perceive it as high risk. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Lack of accuracy of AI output is considered by clinicians and patients to pose a potential risk of harm.

    “The lack of accuracy of AI output was also considered to pose a potential risk of harm by both clinicians and patients, as, in some cases where the algorithm may output false negative results, it may provide an incorrect sense of reassurance and cause a delay in treatment.”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [61]

    The Discussion (page unavailable) links clinicians’ perception of AI risk to patient safety and the accuracy of recommendations. It does not report patients’ views or describe false negatives causing reassurance and delayed treatment. The Results (page unavailable) also say clinicians generally disagreed that the specific system studied put patients at risk. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • When an AI algorithm is too sensitive and provides false positive results, it may add work and costs to the treatment process.

    “However, in cases where the algorithm is too sensitive, thereby providing false positive results, it may add work and costs to the treatment process [39,42,57,61,68].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [61]

    The Results discuss clinicians’ perceptions of the Blood Utilization Calculator’s risk and its effect on their work pace, but do not address overly sensitive algorithms or false positives. Searches for false positives, sensitivity, extra work, and treatment costs found no passage linking those effects in our copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 3

  1. [13]Taylor D Bury M Campling N Carter S Garfied S Newbould J A review of the use of the Health Belief Model (HBM), the Theory of Reasoned Action (TRA), the Theory of Planned Behaviour (TPB) and the Trans-Theoretical Model (TTM) to study and predict health related behaviour change. London, UK: National Institute for Health and Clinical Excellence (2006). 1– 215.
  2. [44]Susanto TD Goodwin R. User acceptance of SMS-based egovernment services. International Conference on Electronic Government. Springer (2011), p. 75– 87.
  3. [48]Sundar SS Waddell TF Jung EH. The Hollywood robot syndrome media effects on older adults’ attitudes toward robots and adoption intentions. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE (2016), pp. 343– 50.

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Authors
Choudhury, A.
Affiliations
West Virginia University (US)
Corresponding author
Choudhury, Avishek
ORCID
Choudhury, Avishek
Venue
Frontiers in Digital Health
Publisher
Frontiers Media SA
Published
2022-08-16
Volume
4
Pages
920662-920662
ISSN
2673-253X (ISSN-L)
DOI
10.3389/fdgth.2022.920662
PMC
PMC9628998
OpenAlex
W4308554392
Cited by
64
References
48
Citations by year
2026: 15 · 2025: 27 · 2024: 15 · 2023: 6 · 2022: 1
FWCI
3.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
artificial intelligence, clinical decision support systems, technology adoption, intention to use, risk perception, AI perception, expectancy, red blood cell transfusion, electronic health records, human factors

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