Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory

Objective Empathy between doctors and patients is crucial in enhancing patient satisfaction with medical consultations. This study, grounded in empathy theory, employs natural language processing and machine learning algorithms to explore the factors influencing patient satisfaction in online healthcare services, particularly the impact of doctor–patient empathy. Methods Utilizing the three dimensions of the Jefferson Scale of Physician Empathy, seven variables were extracted from patient–doctor dialogs as independent variables, with patient satisfaction as the dependent variable. Employing machine learning algorithms, a classification model was constructed to identify the best-fitting model for exploring the pivotal factors influencing patient satisfaction in online medical services. The optimal model was then chosen to investigate the essential factors impacting patients’ satisfaction with online healthcare. Results A total of 7586 data points were collected, with 5447 consultation dialogs (71.8%) receiving a satisfactory rating from patients. LightGBM emerged as the best-performing model, achieving an F1 score of 0.78 and an area under the curve value of 0.81. Factors within the Standing in Patient's Shoes and Perspective Taking dimensions were identified as key determinants of patient satisfaction in online healthcare services. Conclusion This study broadens the conventional scope of applying empathy theory, signifying its crucial role in cultivating doctor–patient empathy within the realm of online healthcare and elevating the overall quality of medical services. The findings indicate that two pivotal factors influencing patients’ satisfaction with online healthcare are doctors’ perceived competence and ability to empathize, understanding patients’ perspectives, and offering assistance.