Exploring the impact of online information signals in leveraging the economic returns of physicians

A Bibliometric Review of Constituents, Themes, and Trends in Online Medical Consultation Research

As an emerging form of health care with accelerated growth in recent years, online medical consultation (OMC) has received extensive attention worldwide. Although the number of studies on OMC has increased substantially, few provide a comprehensive and up-to-date review of OMC's research constituents, themes, and trends. This study, therefore, extracted 1,801 OMC-related articles published in English from the Web of Science (WoS) Core Collection database during the past 30 years and employed a bibliometric analysis of WoS and CiteSpace to examine major constituents' distribution, collaboration relationships, themes, and trends. The results indicate that the United States, England, and China contributed the most to the proliferation of OMC studies. The United States had the greatest academic influence and the most collaborative connections, while China demonstrated the sharpest increase and most active development in recent years. However, there is a lack of substantial and close collaboration between researchers worldwide. The main themes of OMC research were Internet hospitals, COVID-19, mixed methods, online health community, and information technology. Researchers have recently shifted their attention to social media, management, efficacy, word of mouth, mental health, and anxiety. This review paper provides researchers and practitioners with a holistic and clear understanding of the features and trends of OMC research. It also identifies potential areas for future OMC research and sheds light on OMC practices.

Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example

Introduction

Throughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.

Methods

This study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.

Results

Users primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased-especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.

Discussion

This study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.

Combined effects of cues influencing patients’ purchasing behavior in online health-care communities: qualitative comparative analysis based on cue utilization theory

Background

The sudden outbreak of COVID-19 in early 2020 pushed the online health-care communities (OHCs) into the public eye in China. However, OHCs is an emerging service model, which still has many problems such as low patient trust and low patient utilization rate. Patients are the users and recipients of web-based medical services, as well as the core of medical services. Thus, based on cue utilization theory, this paper studies combination effect of influencing factors in patients’ purchase of web-based medical services through the qualitative comparative analysis method of fuzzy sets (fsQCA).

Methods

This paper discards statistical methods based on variance theory-based relationships between explanatory and explained variables and uses a construct theory-based fuzzy set qualitative comparative analysis (fsQCA) approach to elucidate such complex relationships of patients' online purchasing behavior. We use a crawler to automatically download information from Haodf.com. This study crawled data in August 2020, involving 1210 physicians.

Results

Service price, reputation and service quality are the key factors for patients’ purchasing behavior. Physician’s online reputation, online medical service price, number of published articles, mutual-help group, and appointment registration affect patients' purchasing behavior by means of weighted variation. Only when a high scope of internal attribute-related cue elements and a low scope of external attribute-related cue elements are combined with each other in a specific form, patients will generate purchase behavior.

Conclusion

This paper clarifies the complex causes that promote to patients' purchasing behavior of web-based medical services, enriches and develops the relevant theories in the field of consumer purchasing behavior and online health-care communities market research, and has implications for governments, platforms, physicians and patients in the event of web-based medical service purchases.

Internal or External Word-of-Mouth (WOM), Why Do Patients Choose Doctors on Online Medical Services (OMSs) Single Platform in China?

(1) Background: Word-of-mouth (WOM) can influence patients' choice of doctors in online medical services (OMSs). Previous studies have explored the relationship between internal WOM in online healthcare communities (OHCs) and patients' choice of doctors. There is a lack of research on external WOM and position ranking in OMSs. (2) Methods: We develop an empirical model based on the data of 4435 doctors from a leading online healthcare community in China. We discuss the influence of internal and external WOM on patients' choice of doctors in OMSs, exploring the interaction between internal and external WOM and the moderation of doctor position ranking. (3) Results: Both internal and external WOM had a positive impact on patients' choice of doctors; there was a significant positive interaction between internal and third-party generated WOM, but the interaction between internal and relative-generated WOM, and the interaction between internal and doctor-generated WOM were both nonsignificant. The position ranking of doctors significantly enhanced the impact of internal WOM, whereas it weakened the impact of doctor recommendations on patients' choice of doctors. (4) The results emphasize the importance of the research on external WOM in OMSs, and suggest that the moderation of internal WOM may be related to the credibility and accessibility of external WOM, and the impact of doctor position ranking can be explained by information search costs.

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.

From "Human-to-Human" to "Human-to-Non-human" - Influence Factors of Artificial Intelligence-Enabled Consumer Value Co-creation Behavior

The emergence of artificial intelligence (AI) has changed traditional methods of value co-creation. Diverging from traditional methods, this study discusses the influencing factors of AI-supported consumer value co-creation from the perspective of human-to-non-human interactions. This study adopts the stimulus-organism-response framework with consumer engagement (CE) as the intermediary to explore the impact of consumers' personal subjective factors, community factors, and perceptions of AI technology on their value co-creating behaviors. Data were collected from 528 respondents from the Huawei Huafen Club, Xiaomi BBS, Apple China Virtual Brand, Micromobile Phone, and Lenovo communities. SPSS Amos software was used for statistical analysis, revealing that perceived personalization, autonomy, community identity, trust in AI, and self-efficacy are motivational factors that have significant effects on consumer value co-creation behaviors, in which CE plays a significant intermediary role. Our study contributes to the literature on consumer value co-creation supported by AI technology. We also offer important insights for developers of AI-enabled products and service managers.

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.

Examining Different Factors in Web-Based Patients' Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

Exploring the Impact of Linguistic Signals Transmission on Patients' Health Consultation Choice: Web Mining of Online Reviews

BACKGROUND

Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs).

METHODS

Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers' decision making. The hypotheses are tested using 5521 physicians' six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients' opinions regarding their treatment choice.

RESULTS

The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients' decision-making. The influence of negative sentiment, review depth on patients' treatment choice was indirectly mediated by information helpfulness.

CONCLUSIONS

This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.

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.

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.

Not just for the money? An examination of the motives behind physicians’ sharing of paid health information

Online platforms make it possible for physicians to share online information with the public, however, few studies have explored the underlying mechanism of physicians’ sharing of paid health information. Drawing on motivation theory, this study developed a theoretical framework to explore the effects of extrinsic motivation, enjoyment, and professional motivation on the sharing of paid information, as well as the contingent role of income ratio (online to offline) and online reputation. The model was tested with both objective and subjective data, which contain responses from 298 physicians. The results show that extrinsic motivation, enjoyment, and professional motivation play significant roles in inducing physicians to share paid information. Furthermore, income ratio can moderate the effects of motives on paid information sharing. Besides, the effect of professional motivation can be more effective in certain situations (low-level income ratio or high online reputation). This study contributes to the literature on knowledge sharing, online health behaviour, and motivation theory, and provides implications for practitioners.

Seeking or contributing? Evidence of knowledge sharing behaviours in promoting patients' perceived value of online health communities

BACKGROUND

Health knowledge, as an important resource of online health communities (OHCs), attracts users to engage in OHCs and improve the traffics within OHCs, thereby promoting the development of OHCs. Seeking and contributing health knowledge are basic activities in OHCs and are helpful for users to solve their health-related problems, improve their health conditions and thus influence their evaluation of OHCs (ie perceived value of OHCs). However, how do patients' health knowledge seeking and health knowledge contributing behaviours together with other factors influence their perceived value of OHCs? We still have little knowledge.

OBJECTIVE

In order to address the above gap, we root the current study in social cognitive theory and prior related literature on health knowledge sharing in OHCs and patients' perceived value. We treat health knowledge seeking and health knowledge contributing behaviours as behavioural factors and structural social capital as an environmental factor and explore their impacts on patients' perceived value of OHCs.

DESIGN

We have built a theoretical model composed of five hypotheses. We have designed a questionnaire composed of four key constructs and then collected data via an online survey.

SETTING AND PARTICIPANTS

We have distributed the questionnaire in two Chinese OHCs. We obtained a sample of 352 valid responses that were completed by patients having a variety of conditions.

RESULTS

The empirical results indicate that health knowledge seeking and health knowledge contributing have positive impacts on patients' perceived value of OHCs. The impact of health knowledge seeking on patients' perceived value of OHCs is greater than the impact of health knowledge contributing. In addition, structural social capital moderates the effects of health knowledge seeking and health knowledge contributing on patients' perceived value of OHCs. It weakens the effect of health knowledge seeking but enhances the effect of health knowledge contributing on patients' perceived value of OHCs.

CONCLUSIONS

These findings contribute to the literature on patients' perceived value of OHCs and on the role of structural social capital in OHCs. For OHC managers, they should provide their users more opportunities to seek or contribute health knowledge in their communities.