Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda

Yang, Yikai; Ngai, Eric W.T.; Wang, Lei

doi:10.1016/j.im.2024.103961

Cited by 13 publications

(2 citation statements)

References 128 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hesitation of managers to fully adopt AI, due to its early stage of implementation, reflects an important barrier that has also been observed in other research [32,33] . This point underscores the critical need to strengthen the evidence base supporting the efficacy of AI in health management contexts [30,37,38] .…”

Section: Discussionmentioning

confidence: 99%

“…The need to deal with uncertainties related to the use and integration of AI, and the ambiguity of AI contributes to distrust among professionals [47] . Thus, it is imperative that AI implementation processes be accompanied by clear and effective change management strategies, aiming to minimize organizational resistance and maximize the acceptance of technology and innovation [32,37,47] .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Artificial Intelligence tools in supporting decision-making in hospital management

Alves,

Seringa,

Silvestre

et al. 2024

Preprint

View full text Add to dashboard Cite

Background The integration of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers' perceptions of AI as a decision-support tool. Methods A descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses. Results Hospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration. Conclusions The study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

show abstract

Section: Discussionmentioning

confidence: 99%