Abstract
(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.
Highlights
Understanding patient preferences of service quality is vital for the healthcare industry and healthcare providers to develop optimal strategies to improve patients’ quality of care [1]
physician online reviews (PORs) are a significant source of knowledge for many patients who are looking for a good doctor [5]. ey see these physicians rating websites (PRWs) as an important source for finding the best doctor [6, 7]. ese PORs offer authentic information for patients’ wellbeing but likewise contribute to an evolving relationship between doctors and their patients [8, 9]
We focus on multimethod analysis, including implicit and domain knowledge-based specialized sentic computing emotion analysis and econometric approach to predict physician review helpfulness (RH). e proposed multimethod model shows an excellent performance with a classification accuracy of 91.12%
Summary
Understanding patient preferences of service quality is vital for the healthcare industry and healthcare providers to develop optimal strategies to improve patients’ quality of care [1]. With the growing popularity of physicians rating websites (PRWs), better information can be obtained regarding factors influencing patients’ choices of selecting the right doctor [2]. Unlike traditional surveys used to collect information on patients’ preferences and treatment experiences, physician online reviews (PORs) offer a rich source of knowledge without the interventions by researchers or healthcare organizations [3]. PORs are a significant source of knowledge for many patients who are looking for a good doctor [5]. Ey see these PRWs as an important source for finding the best doctor [6, 7]. Ese PORs offer authentic information for patients’ wellbeing but likewise contribute to an evolving relationship between doctors and their patients [8, 9]. PORs alleviate the overall choice burden on users, they trigger many issues, such as presenting misguided or inappropriate information [10].
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