Enabling the Adoption of Machine Learning in Clinical Decision Support: A Total Interpretive Structural Modeling Approach

It has been reported that the healthcare industry is the slowest adopter of artificial intelligence methods, particularly machine learning, compared to other industries. However, machine learning can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness This paper aims to identify the enablers for adopting machine learning in supporting clinical decision-making and propose the strategic road map towards boosting the clinicians' intentions to adopt machine learning as a clinical decision support tool. This paper utilizes the TISM methodology and the MICMAC analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision making . The study found that building an academic foundation, raising the awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians’ intentions to adopt machine learning in supporting clinical decision making.

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Published in: Informatics in Medicine Unlocked
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://doi.org/10.1016/j.imu.2022.101090