What patients like or dislike in physicians: Analyzing drivers of patient satisfaction and dissatisfaction using a digital topic modeling approach

Shah, Adnan Muhammad; Yan, Xiangbin; Tariq, Samia; Ali, Mudassar

doi:10.1016/j.ipm.2021.102516

Cited by 55 publications

(30 citation statements)

References 55 publications

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“…This study analyzed PORs datasets from PRWs in the U.S. Website regulators, and healthcare officials monitor the posting of PORs in order to avoid the postings of rumors and misinformation from public. Since previous studies mostly focused on the Twitter data for social media analysis of different pandemics [ 8 , 14 , 21 , 65 ], to the authors’ knowledge, there are limited studies in the U.S. context which incorporated social media analytics of public response expressed in PORs across multiple years, including the current period of COVID-19 disease outbreak. Social media research provides this potential because of the extensive usage of these channels and the relative speed at which data can be collected [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…Because of their sheer number, PRWs data can also aid in identifying and tracking unusual event events in public, such as the multisystem inflammatory syndrome linked with COVID-19 [ 9 ]. Moreover, PRWs provide a low-cost and productive forum for evaluating the efficacy of public health messaging and focusing public health efforts on the prevailing topics of PRW discussion [ 8 ]. Using ML to analyze PORs may also reveal how the public interprets mixed signals about various healthcare issues.…”

Section: Discussionmentioning

confidence: 99%

“…Through analyzing texts from real-time media, the opinions and sentiments of the subconscious-minded individuals are gathered in order to understand the true cognitive ideas of people [ 7 ]. Patients post physician online reviews (PORs) on PRWs, whereas other patients follow them to evaluate a physician quality [ 8 ]. Since the utilization of social media is so common among the public, the unique advantages would make it easier for the public to access and communicate information, resulting in an effective response to the crisis [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Shah

Jeong

2021

IJERPH

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(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients’ feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Shah

Jeong

2021

IJERPH

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“…Prior research has indicated that service personnel and the core product/ service are the vital cues for the consumers to evaluate their service experience; positive evaluations of service personnel and product/service may lead to favorable evaluations of service encounters (Greenwell et al, 2002). Health-care professionals are the employees who make direct contact with patients and deliver the core service in the health-care industry; therefore, their professionalism and the ability to deal with service failures have significant influences on consumer evaluation of the service encounter (Shah et al, 2021;Bassett et al, 1997). While social practices such as provider professionalism and service recovery measures shape the patients service experience, the goal is to ensure the patients is delighted and satisfied with the service encounter (Gustavsson, 2014).…”

Section: Mediating Role Of Service Experiencementioning

confidence: 99%

Operations-oriented strategies and patient satisfaction: the mediating effect of service experience

Boakye

Qin

Blankson

et al. 2021

IJQSS

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Purpose The purpose of this study is to explore the direct and indirect effects of perceived provider professionalism and service recovery in enhancing patient satisfaction in a developing country. Design/methodology/approach This study used a survey method to investigate satisfaction among health-care consumers. This study used data collected from 210 health-care consumers to empirically test the hypotheses via structural equation modeling Findings This study found that service recovery has a significant direct effect on patient satisfaction. Though this study did not find perceived provider professionalism to have a direct effect on patient satisfaction, it found an indirect effect in the relationship via service experience. Thus, service experience fully/completely mediates the relationship between perceived provider professionalism and patient satisfaction, while partially mediating the significant relationship between service recovery and patient satisfaction. Originality/value The results further underscore the need for health-care organizations in developing countries to focus on mindfully developing operations-oriented strategies that lead to the delivery of memorable service experiences for patients.

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“…For quite a while, the research topic of RH has drawn academic interest in the search and experience goods context [ 19 , 20 , 24 ]. The idea of RH has also gained researchers' attention in the healthcare domain, which is categorized as credence goods.…”

Section: Introductionmentioning

confidence: 99%

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

Shah

Muhammad

Lee

2022

Computational Intelligence and Neuroscience

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(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.

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What patients like or dislike in physicians: Analyzing drivers of patient satisfaction and dissatisfaction using a digital topic modeling approach

Cited by 55 publications

References 55 publications

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

Operations-oriented strategies and patient satisfaction: the mediating effect of service experience

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

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