Home healthcare (HHC) is a beneficial choice for many people and especially an essential alternative to clinics and hospitals for infection prevention during the COVID-19 pandemic. Moreover, patient trust in HHC providers is critical to care success and highly affects patient satisfaction. In this paper, an intelligent algorithm is proposed to assess the performance of an HHC center considering trust indicators. For this purpose, the effect of these indicators on patient satisfaction was examined. First, the required data is collected from patients who received care from the HHC service under study through two validated questionnaires containing items related to trust and patient satisfaction. Efficiency scores for each decision-making unit were computed using an artificial neural network and statistical methods. Based on each trust indicator, sensitivity analysis and statistical tests were conducted to evaluate the (in) appropriateness of HHC center performance. In addition, a strengths-weaknesses-opportunities-threats analysis is conducted to suggest strategies for improving the HHC center performance. The algorithm was validated using the data envelopment analysis method. As far as we know, this is the first study to evaluate the performance of HHC centers based on trust indicators, and the model presented in this study can be implemented in other healthcare units to enhance patient satisfaction.
Keywords: Artificial neural network; Data envelopment analysis; Home healthcare; Patient satisfaction; Performance evaluation; Trust.
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