Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US

Abstract

Effective integration, use, and adoption of Artificial Intelligence (AI) into the healthcare system will require human factors considerations and a systems approach in addition to predictive accuracy. This exploratory study focuses on clinicians' perception of the role of accountability, training and their impact on the intention of using AI and associated decision making. The study also captures the perception of clinicians on the role of AI on workload, trustworthiness, risk, and performance expectancy. A semi-structured survey, including itemized and open-ended questions, was distributed to healthcare practitioners working in the United States. Data were collected using an audience paneling company. A screening question exclusively selected healthcare professionals working actively within the United States of America. The study leveraged sequential regression and inductive content analysis to analyze quantitative and qualitative survey responses. Two hundred and sixty-five participants completed the survey. The findings showed a significant impact of various variables, including perceived workload, perceived trustworthiness of AI, perceived risk of AI, and willingness to receive AI training on using AI. Lack of AI accountability was also identified as an inhibiting factor in AI use. Lastly, the study found that performance expectancy, perceived risk, and trustworthiness influence practitioners’ perception of AI's impact on decision-making.