An Intelligent Framework for Performance Optimization of Telemedicine Center with Trust incorporating decision-making styles

Telemedicine has emerged as a major alternative for in-person visits, offering numerous benefits for both patients and healthcare providers. Additionally, trust is a key factor that significantly impacts treatment outcomes and patient satisfaction. This paper introduces a comprehensive approach to assess the performance of a telemedicine center considering trust and patient satisfaction indicators and investigate the optimal combination of demographic characteristics and patient decision-making styles. To achieve this, an intelligent algorithm composed of an artificial neural network (ANN) combining with the mountain gazelle optimizer (MGO) algorithm and statistical method was employed. The required data is collected from the patients of the telemedicine center under study using standard questionnaires. Sensitivity analysis and statistical tests were utilized to evaluate the performance of the telemedicine center and data envelopment analysis was utilized to validate the model. The results of this study indicate that married male patients between 35 and 50 years of age, with a Master’s degree, average financial status, unspecified insurance status and flexible decision-making style show the highest level of trust in mentioned telemedicine center. Also, a SWOT (strengths, weaknesses, opportunities, and threats) analysis is implemented to develop applicable strategies to enhance the performance of the center. To the extent of our knowledge, this is the first study that assesses the performance of a telemedicine center using trust and patient satisfaction indicators, along with exploring the optimal combination of demographic characteristics and patient decision-making styles; and the model proposed in this study can be used in other healthcare centers to improve the performance.