Fuzzy Multi-Criteria Decision Analysis Model for Cost-Risk Tradeoff in Healthcare Automation Investments

Strategic decisions about the automation investments in health care organizations present stakeholders with many challenges for identifying superior solutions, needed methods which would allow evaluating costs and risks across various factors. This work presents an FMCDA model which is designed to deal with the uncertainty observation in healthcare automation investment evaluation. The evaluation data is uncertain which is overcome through the use of fuzzy set theory and the criteria weighting is done by using the Analytic Hierarchy Process. To support these claims, the model was first validated based on case studies obtained from three real hospitals that were considering the adoption of RPA. Using the recursive procedure of the proposed FMCDA, optimal investment options have been predicted with a success rate of 87.5% on average by means of comparing them with post-hoc evaluation of implementation performances. Probability and sensitivity analysis was performed with changes in the specified parameter ±15%, by which, the decisions were consistent in 82% of the cases. This marks the end of the study, which offers a novel framework to help the healthcare administrations to reduce cost-risk implications while making automation investment decisions and ultimately, enhance resource utilization efficiency.