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

A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset

BACKGROUND

Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups.

METHODS

For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018.

RESULTS

The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018.

CONCLUSION

Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.

Medical service quality evaluation based on LDA and sentiment analysis: Examples of seven chronic diseases

Objective

In this article, we investigate how chronic noncommunicable disease (CND) patients evaluate the medical service, and what obstacles exist in this process, which is useful for hospitals to improve efficiency and enhance patient satisfaction.

Methods

Based on the total number of CND patients in China, 7 CNDs were selected as the evaluation objects, and then selected the Haodaifu website as the data source, crawled 15,682 medical service reviews, then the 9 themes were analyzed by the LDA theme model. The evaluation index system of six indicators was constructed based on quality management theory. The binary long short-term memory model was used to analyze the sentiment, and the entropy-valued, TOPSIS and gray correlation model was implemented for medical service quality evaluation; the barrier model was used to find out the key factors limiting medical services.

Results

(a) Hypertension was rated at a good level in the degree of gray correlation closeness, bronchitis was rated at a low level and the rest were at an intermediate level. (b) The first two overall barriers were the hospitalization process and registration services which occupy about 30%, respectively. This implies that hospitals should focus on providing registration services and inpatient settings in the future.

Conclusion

To promote hospitals to provide better services for patients with CNDs and improve patient satisfaction with medical care. And it is necessary to optimize medical services fundamentally by optimizing the inpatient process and improving the registration process to improve efficiency.

Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews

Introduction: Use of both in-person and video visits have become a common norm in health care delivery, especially after the COVID-19 pandemic. It is imperative to understand how patients feel about their providers and their experiences during in-person and video visits. This study examines the important factors that patients use in their reviews and differences in the relative importance. Methods: We performed sentiment analysis and topic modeling on online physician reviews from April 2020 to April 2022. Our dataset comprised 34,824 reviews posted by patients after completing in-person or video visits. Results: Sentiment analysis yielded 27,507 (92.69%) positive and 2,168 (7.31%) negative reviews for in-person visits, and 4,610 (89.53%) positive and 539 (10.47%) negative reviews for video visits. Topic modeling identified seven factors patients used in their reviews: Bedside manners, Medical Expertise, Communication, Visit Environment, Scheduling and Follow-up, Wait times, and Costs and insurance. Patients who gave positive reviews after in-person consultations more frequently mentioned communication, office environment and staff, and bedside manners. Those who gave negative reviews after in-person visits mentioned longer wait times, providers' office and staff, medical expertise, and costs and insurance problems. Patients with positive reviews after video visits emphasized communication, bedside manners, and medical expertise. However, patients posting negative reviews after video visits frequently mentioned problems with appointment scheduling and follow-up, medical expertise, wait times, costs and insurance, and technical problems in video visits. Conclusions: This study identified key factors that influence patients' assessment of their providers in in-person and video visits. Paying attention to these factors can help improve the overall patient experience.

Face time with physicians: How do patients assess providers in video-visits?

Introduction

The COVID-19 pandemic has triggered a massive acceleration in the use of virtual and video-visits. As more patients and providers engage in video-visits over varied digital platforms, it is important to understand how patients assess their providers and the video-visit experiences. We also need to examine the relative importance of the factors that patients use in their assessment of video-visits in order to improve the overall healthcare experience and delivery.

Methods

A data set of 5149 reviews of patients completing a video-visit was assembled through web scraping. Sentiment analysis was performed on the reviews and topic modeling was used to extract latent topics embedded in the reviews and their relative importance.

Results

Most patient reviews (89.53%) reported a positive sentiment towards their providers in video-visits. Seven distinct topics underlying the reviews were identified: bedside manners, professional expertise, virtual experience, appointment scheduling and follow-up process, wait times, costs, and communication. Communication, bedside manners and professional expertise were the top factors patients alluded to in the positive reviews. Appointment-scheduling and follow-ups, wait-times, costs, virtual experience and professional expertise were important factors in the negative reviews.

Discussion

To improve the overall experience of patients in video-visits, providers need to engage in clear communication, grow excellent bedside and webside manners, promptly attend the video-visit with minimal delays and follow-up with patients after the visit.

Switching from offline to online health consultation in the post-pandemic era: the role of perceived pandemic risk

Introduction

Due to its effectiveness and various benefits, the use of online health consultation (OHC) has dramatically increased in recent years, especially since the outbreak of the COVID-19 pandemic. However, underlying mechanism whereby the pandemic impacted OHC usage is still unclear.

Methods

Via an online survey (N=318), the present paper measures the users' perceptions towards both offline and online services, their intention to switch to OHC, and the perceived pandemic risks. The relationships among these factors are conceptualized by the push-pull-mooring framework, and tested via structural equation modelling.

Results

Dissatisfaction with offline service (process inefficiency and consultation anxiety), the attractiveness of OHC (perceived benefits and perceived ease of use), and users' behavioral inertia (switching cost and habit) jointly influence the intention to switching to OHC. The significant role of the perceived pandemic risk of going to medical facilities is particularly addressed. On the one hand, the perceived pandemic risk is found with an indirect impact on the switching intention by enlarging the dissatisfaction with offline service and the attractiveness of OHC. On the other hand, a high perceived pandemic risk induces more actual switching behavior and also amplifies the transition from switching intention to behavior.

Discussion

The study provides novel insights into the understanding of OHC usage in the post-pandemic era, and also informs medical facilities, OHC platforms, and policymakers on managing and balancing the online and offline healthcare provision.

Patient Engagement as Contributors in Online Health Communities: The Mediation of Peer Involvement and Moderation of Community Status

This study focuses on patient engagement in online health communities (OHCs) and investigates the mechanism related to the impact of social support provided by patients on their personal engagement. Based on social support theory, we put forward a research model and conduct empirical analysis using datasets of 4797 patients with 160,484 posts and 1,647,569 replies from an online health community in China. The mediation of peer involvement and moderation of community status are also examined. The results indicate that the subdimensions of social support positively influence patient engagement with informational support exerting the greatest impact. Peer patient involvement imposes significant partial and positive mediating effects on the relationships, especially on informational support. Community status negatively moderates the impacts of social interactions and informational support on patient engagement in that the influence of social interactions and informational support are more profound for patients with low community status. The findings can bring an understanding of patient engagement in OCHs, and provide theoretical and practical implications to facilitate the development of an online healthcare service.

Research on emergency material demand based on urgency and satisfaction under public health emergencies

In recent years, the frequent occurrence of public health emergencies has had a significant impact on people's life. The study of emergency logistics has also attracted scholars' attention. Therefore, matching emergency materials' supply and demand quickly, which meets urgency and satisfaction, is the purpose of this paper. This paper used the Metabolism Grey Model (1,1) (GM (1,1)) and the material demand prediction model to predict the number of infections and material demand. Besides, we established a bi-objective optimization model by constructing a profit and loss matrix and a comprehensive utility perception matrix. The results show that the method is helpful in matching the optimal supply and demand decision quickly on the basis of meeting urgency and satisfaction. The method is helpful in improving the fairness of emergency material distribution, which could better protect people's livelihoods.

Qualitatively exploring the impact of a relationship-centered communication skills training program in improving patient perceptions of care

Objective

To explore qualitative patient experience comments before and after a relationship-centered communication skills training to understand patient experience, program impact, and opportunities for improvement.

Methods

Qualitative patient experience evaluation data was captured from January 2016 to December 2018 for 483 health care clinicians who participated in the skills training. A random sampling of available open-ended patient comments (N = 33,223) were selected pre-training (n = 668) and post-training (n = 566). Comments were coded for valence (negative/neutral/positive), generality versus specificity, and based on 12 communication behaviors reflective of training objectives.

Results

No significant difference was found in the valence of comments, or generality versus specificity of comments before and after the training. A significant decrease was present in perceived clinician concern. "Confidence in care provider" was the communication skill most frequently identified in comments both pre- and post-training.

Conclusion

Perceptions of interactions largely remained the same following training. Key relationship-centered communication skills require further attention in future training efforts. Measurements of patient satisfaction and engagement may not adequately represent patient experience.

Innovation

This study identified areas for improvement in the training program and offers a model for utilizing patient experience qualitative data in understanding communication training impact.

Measuring knowledge contribution performance of physicians in online health communities: A BP neural network approach

Extant literature on measuring the performance of physicians’ knowledge contribution in an online health community (OHC) is limited. To address this gap, this article aims to (1) develop a measurement model for physicians’ knowledge contribution performance; (2) use BP neural network to assign reasonable weight to each indicator of the model; and (3) explore the status and differences of knowledge contribution performance among a group of physicians. Based on the sample of 5407 infectious disease physicians in a Chinese OHC, we propose the measurement model by integrating physicians’ active knowledge contribution (AKC) and responsive knowledge contribution (RKC), covering 11 dimensions of contribution quantity and quality. We employ the BP neural network to optimise the model weights using the initial weight of the model obtained by the entropy method. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to evaluate the performance of physicians’ knowledge contribution in the OHC. The results show that it is feasible to use BP neural network to assign model weights. The distribution of physicians’ knowledge contribution performance is uneven; only a few have a high-level knowledge contribution performance. Meanwhile, a significant positive correlation exists between a physician’s title and respective knowledge contribution performance. Our research may contribute to related literature and practices by offering a fine-grained understanding of the performance of physicians’ knowledge contribution.

Investigating the effect of service feedback and physician popularity on physician demand in the virtual healthcare environment

Purpose

This study examines how service feedback and physician popularity affect physician demand in the context of virtual healthcare environment. Based on the signaling theory, the critical factor of environment uncertainty (i.e. disease risk) and its impact on physician demand is also investigated. Further, the research on the endogeneity of online reviews in healthcare is also examined in the current study.

Design/methodology/approach

A secondary data econometric analysis using 3-wave data sets of 823 physicians obtained from two PRWs (Healthgrades and Vitals) was conducted. The analysis was run using the difference-in-difference method to consider physician and website-specific effects.

Findings

The study's findings indicate that physician popularity has a stronger positive effect on physician demand compared with service feedback. Improving popularity leads to a relative increase in the number of appointments, which in turn enhance physician demand. Further, the impact of physician popularity on physician demand is positively mitigated by the disease risk.

Originality/value

The authors' research contributes to a better understanding of the signaling transmission mechanism in the online healthcare environment. Further, the findings provide practical implications for key stakeholders into how an efficient feedback and popularity mechanism can be built to enhance physician service outcomes in order to maximize the financial efficiency of physicians.

Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities

How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic (doctor's academic rank and practice name), “research ability,” “foreign experience,” “committee position,” and “clinical experience” were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their “research ability” and “foreign experience” had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more “clinical experience” had a significant negative impact on physician ratings. Moreover, while descriptions about “foreign experience” and “committee position” had a significant positive impact on page views, physician mentions of “research ability” had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles.

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

(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.

Analysis of Destination Images in the Emerging Ski Market: The Case Study in the Host City of the 2022 Beijing Winter Olympic Games

This study aims to propose a text mining framework suitable for destination image (DI) research based on UGC (User Generated Content), which combines the LDA (Latent Dirichlet Allocation) model and sentiment analysis method based on custom rules and lexicon to identify and analyze the DI in the emerging ski market. The ski resorts in the host city of the 2022 Winter Olympic Games are selected as a case study. The findings reveal that (1) 9 image attributes, out of which two image attributes have not been identified before in winter destination studies, namely beginner suitability and ticketing service. (2) In the past seven snow seasons, the negative sentiment of tourists has shown a continuous downward trend. The positive sentiment has exhibited a slow upward trend. (3) For tourists from destination countries affected by the Winter Olympic Games, the destination image will be improved when the destination meets their expectations. When the destination cannot meet their expectations, the tourists still believe that the holding of the Winter Olympic will enhance the destination’s situation. The theoretical and managerial implications of these findings are discussed.

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

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.

The Impact of Signals Transmission on Patients' Choice through E-Consultation Websites: An Econometric Analysis of Secondary Datasets

(1) Background: The COVID-19 pandemic has dramatically and rapidly changed the overall picture of healthcare in the way how doctors care for their patients. Due to the significant strain on hospitals and medical facilities, the popularity of web-based medical consultation has drawn the focus of researchers during the deadly coronavirus disease (COVID-19) in the United States. Healthcare organizations are now reacting to COVID-19 by rapidly adopting new tools and innovations such as e-consultation platforms, which refer to the delivery of healthcare services digitally or remotely using digital technology to treat patients. However, patients' utilization of different signal transmission mechanisms to seek medical advice through e-consultation websites has not been discussed during the pandemic. This paper examines the impact of different online signals (online reputation and online effort), offline signals (offline reputation) and disease risk on patients' physician selection choice for e-consultation during the COVID-19 crisis. (2) Methods: Drawing on signaling theory, a theoretical model was developed to explore the antecedents of patients' e-consultation choice toward a specific physician. The model was tested using 3-times panel data sets, covering 4231 physicians on Healthgrades and Vitals websites during the pandemic months of January, March and May 2020. (3) Results: The findings suggested that online reputation, online effort and disease risk were positively related to patients' online physician selection. The disease risk has also affected patients' e-consultation choice. A high-risk disease positively moderates the relationship between online reputation and patients' e-consultation choice, which means market signals (online reputation) are more influential than seller signals (offline reputation and online effort). Hence, market signals strengthened the effect in the case of high-risk disease. (4) Conclusions: The findings of this study provide practical suggestions for physicians, platform developers and policymakers in online environments to improve their service quality during the crisis. This article offers a practical guide on using emerging technology to provide virtual care during the pandemic. This study also provides implications for government officials and doctors on the potentials of consolidating virtual care solutions in the near future in order to contribute to the integration of emerging technology into healthcare.

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

(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.

Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach

INTRODUCTION

An increasing number of patients are voicing their opinions and expectations about the quality of care in online forums and on physician rating websites (PRWs). This paper analyzes patient online reviews (PORs) to identify emerging and fading topics and sentiment trends in PRWs during the early stage of the COVID-19 outbreak.

METHODS

Text data were collected, including 55,612 PORs of 3430 doctors from three popular PRWs in the United States (RateMDs, HealthGrades, and Vitals) from March 01 to June 27, 2020. An improved latent Dirichlet allocation (LDA)-based topic modeling (topic coherence-based LDA [TCLDA]), manual annotation, and sentiment analysis tool were applied to extract a suitable number of topics, generate corresponding keywords, assign topic names, and determine trends in the extracted topics and specific emotions.

RESULTS

According to the coherence value and manual annotation, the identified taxonomy includes 30 topics across high-rank and low-rank disease categories. The emerging topics in PRWs focus mainly on themes such as treatment experience, policy implementation regarding epidemic control measures, individuals' attitudes toward the pandemic, and mental health across high-rank diseases. In contrast, the treatment process and experience during COVID-19, awareness and COVID-19 control measures, and COVID-19 deaths, fear, and stress were the most popular themes for low-rank diseases. Panic buying and daily life impact, treatment processes, and bedside manner were the fading themes across high-rank diseases. In contrast, provider attitude toward patients during the pandemic, detection at public transportation, passenger, travel bans and warnings, and materials supplies and society support during COVID-19 were the most fading themes across low-rank diseases. Regarding sentiment analysis, negative emotions (fear, anger, and sadness) prevail during the early wave of the COVID-19.

CONCLUSION

Mining topic dynamics and sentiment trends in PRWs may provide valuable knowledge of patients' opinions during the COVID-19 crisis. Policymakers should consider these PORs and develop global healthcare policies and surveillance systems through monitoring PRWs. The findings of this study identify research gaps in the areas of e-health and text mining and offer future research directions.