Evaluating AI adoption challenges in healthcare using a Multi-Criteria Decision-Making approach: implications for...

Area of Science:

• Healthcare Analytics
• Artificial Intelligence
• Predictive Modeling

Background:

• Artificial intelligence (AI) adoption in predictive healthcare risk analytics offers transformative potential for clinical decision-making and resource management.
• However, significant socio-technical challenges impede the widespread implementation of AI in healthcare settings.

Purpose of the Study:

• To identify and prioritize key barriers influencing AI adoption in predictive healthcare risk analytics.
• To analyze the causal relationships and relative importance of these barriers.

Main Methods:

• Utilized an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Hierarchy Process (AHP) model.
• Identified fifteen challenges across technological, data-related, organizational, human/social, and ethical-regulatory dimensions through literature review and expert validation.
• Applied DEMATEL for causal analysis and AHP for hierarchical weighting of barriers.

Main Results:

• Key structural drivers impacting AI adoption include data privacy, data quality, AI governance, and system interoperability.
• Leadership support and strategic alignment are identified as critical organizational enablers.
• Data-related and governance challenges act as primary causal factors, with human and ethical concerns as dependent outcomes.

Conclusions:

• Successful AI adoption in healthcare analytics necessitates strong leadership, robust data governance, and transparent, interoperable technologies.
• Provides a structured roadmap for healthcare organizations to implement scalable and reliable predictive analytics.