An Intelligent Decision-Making Framework for Cloud Adoption in Healthcare: Combining DOI Theory, Machine Learning, and Multi-Criteria Approaches

Abstract

Cloud adoption in healthcare presents many benefits, including the superior accessibility of data, scalability, and cost efficiency. However, it remains to be accompanied by many strenuous challenges, such as security risks, regulatory compliance, interoperability, and organizational resistance to change. To address these complexities, this study proposes an intelligent decision-making framework that integrates DOI theory, machine learning, and MCDM techniques for making optimal cloud adoption decisions. The framework sets off with DOI theory. This assesses the readiness for adoption, and identifies a number of influencing factors that include relative advantage, compatibility, complexity, trialability, and observability. Predictions of cloud adoption probability and hidden patterns in the adoption of cloud services can be uncovered from historical and real-time healthcare data by machine learning algorithms such as decision trees and neural networks. MCDM techniques such as Analytic Hierarchy Process (AHP) and TOPSIS evaluate cloud service providers against multiple factors including cost, security, scalability, compliance, and performance. Results show that the proposed model yields 97% prediction accuracy, 96% decision confidence, and 65% complexity reduction, outperforming the traditional decision-making approach by a large margin. In addition, it enhances cost efficiency (90%), scalability (88%), and security compliance (92%), making the approach robust, adaptable, and data-driven for cloud adoption in healthcare. This study will help in making informed decisions, increasing operational efficiency, and regulatory alignment, which supports the smooth integration of clouds into healthcare organizations.