The Impact of Cultural Dimensions of Clinicians on the Adoption of Artificial Intelligence in Healthcare

ABSTRACT

Introduction: Healthcare is probably the last frontier that Artificial Intelligence (AI) has not conquered. Cultural factors significantly impact the way healthcare is accessed and delivered. Affordability, educational and social status, physician training, lack of physician talent in difficult to serve areas all contribute to this. Cultural perspectives of clinicians and clinical habits during the human-computer interaction and inherent suspicion of lack of human to human interaction contribute to perceptions of inhibition in the adoption of AI in routine medical practice. In this paper we examine whether measurable cultural dimensions would impact the adoption of AI in routine clinical practice. Materials and Methods: Qualified Medical Professionals (n=206) were chosen randomly and an online secure survey was conducted consisting of 26 questions. 83% of respondents were from different parts of India, remaining 17 % from other countries like USA,Canada, UK, UAE, Oman, Zambia, Nigeria, Bangladesh, Vietnam and Japan. We defined four different cultural dimensions inspired by Hofstede’s cultural dimension theory and one dimension based on attitudes of clinicians towards technology in general. We measured the following 1. Compliance distance (the degree of adherence to evidence based standards) 2. Collectivism vs Individualism (the sense of belonging to a group) 3. Long term vs Short term orientation (the idea of planning and thinking long term) 4. Uncertainty Avoidance (the degree of tolerance to uncertainty) 5. Technology Friendliness (the degree to which technology is perceived as being helpful) Results: We found that there were no differences in adoption of AI in clinical practices based on compliance, collectivism, and long term orientation. However, we found a correlation between the requirement for a face to face consultation (high uncertainty avoidance) and Non-adoption of AI. The results demonstrate that uncertainty avoidance hinder the acceptance of technology like telemedicine and AI alike. There were also no major differences in the adoption of AI based on any geographical variation, specialty or practice sector on the adoption of AI. Notably, tech savviness or technology friendliness did not affect the adoption of AI. We conclude that any useful AI technology which gives validated results could be adopted by clinicians in general and has potential to become a good screening measure in areas with poor healthcare access. Conclusion: Of the many cultural dimensions we studied, the only dimension that seemed to have an impact on the adoption of any technology including AI was the high uncertainty avoidance. Other dimensions did not impact the adoption of AI.