A Hybrid Model for Assessing the Performance of Medical Tourism: Integration of Bayesian BWM and Grey PROMETHEE-AL

Abstract

Medical tourism (MT) is the activity of traveling domestically or abroad to receive medical services. The scope of medical treatments covers dentistry, surgery, antiaging procedures, preventive medicine, and even some health-related treatments (meditation, physiotherapy, psychotherapy, addiction treatment, psychiatry, etc.). Due to the global boom in MT, governments are actively promoting MT packages to capture this huge business opportunity. However, what are the key factors that make MT development successful or unsuccessful? How can the performance of current MT operators be evaluated? And, how can the performance of underperforming operators be improved? This paper addresses these questions by proposing a MT assessment framework that summarizes the potential key factors of MT. In addition, this study proposes a model that integrates the Bayesian Best-Worst Method (Bayesian BWM) and grey Preference Ranking Organization Method for Enrichment Evaluations based on Aspiration Level (grey PROMETHEE-AL) to assess the performance of assessed MT operators. The Bayesian BWM not only aggregates the judgments of multiple experts but also generates a set of objective group criteria weights. Besides, the modified PROMETHEE incorporates the grey theory and aspiration level concept to increase the usefulness of the original PROMETHEE. The results of the analysis show that the two most critical criteria for MT are “the operators have cloud computing systems to analyze the travelers’ sensor data in real-time and accurately to provide customized medical services” and “multilingualism and communication skills of medical travel-related personnel.” Poor performers in the travel industry can be improved by prioritizing the criteria in order of importance. The management implications of this study can be used as a basis for performance evaluation by operators and government health care organizations.