How professional capital and team heterogeneity affect the demands of online team-based medical service

The mechanism of word-of-mouth learning on chronic disease patients' physician choice in online health communities: Latent Dirichlet allocation analyses and cross-sectional study

Background

Word-of-mouth learning (WOML) plays a substantial role in patients' physician choice behavior. However, there is still a research gap in analyzing the mechanism of WOML on chronic disease patients' physician choice in online health communities (OHCs) considering individual differences.

Objective

This study aims to develop a physician choice mechanism research model to reveal the influence of WOML on chronic disease patients' physician choice decision process from external interaction to internal cognition and emotion in OHCs based on social learning theory (SLT). The moderating effects of reasons for consultation and patients' demographic characteristics on the model's relationships were also explored.

Methods

Guided by SLT, this study identified the external interaction factors and internal cognitive and emotional factors by analyzing 72,123 patients' online reviews based on a Latent Dirichlet Allocation model and developed the physician choice mechanism research model. The model was validated using structural equation modeling based on an online questionnaire survey of 526 valid Chinese patients with chronic disease. The moderating effect of reasons for medical consultation and demographic characteristics was examined using multi-group analysis.

Results

Status capital (SC), decisional capital (DC), and price value (PV)) were the main external interaction factors to initiating chronic disease patients' internal cognition and emotion (perceived convenience (PC), perceived health benefits (PH), and patients' physician choice intention (CI)). PH and PC significantly mediated the relationship between SC, DC, PV, and CI. Reasons for medical consultation, district, and sex significantly moderated the relationships in the model.

Conclusions

Considering individual differences, the results of this study advance a comprehensive understanding of how chronic disease patients interact with the environment through WOML to make physician choice decisions. OHCs can recommend suitable physician information to chronic disease patients considering individual differences to match patients' demands and improve service quality.

Exploring the Impact of Online Medical Team Engagement on Patient Satisfaction: A Semantic Features Perspective

Online medical teams (OMTs), a new mode of online healthcare service, have emerged in online health communities (OHCs) in China. This study attempts to explore the underlying mechanism of how OMTs' engagement influences patient satisfaction through the lens of semantic features. This study also scrutinizes the moderating effect of multiple specializations on the link between OMTs' engagement and semantic features. We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5000 samples was employed to test the mediation effects. The findings reveal that OMTs' engagement significantly improves language concreteness in online team consultations, which subsequently enhances patient satisfaction. OMT engagement has a negative impact on emotional intensity, ultimately decreasing patient satisfaction. Multiple specializations strengthen the impact of OMT engagement on both language concreteness and emotional intensity. This study contributes to the literature on OMTs and patient satisfaction, providing insights into patients' perceptions of OMTs' engagement during online team consultation. This study also generates several implications for the practice of OHCs and OMTs.

Exploring sources of patient dissatisfaction in mobile health communication: A text analysis based on structural topic model

Understanding online patient dissatisfaction is essential for improving the quality of healthcare services, patient satisfaction, and physician career development. This study is the first to apply the structural topic model to patient satisfaction research based on patient online reviews from a mobile health communication platform, revealing eight negative topics of patient concerns. These topics include under-explored areas such as "go to the hospital for check-ups," "incomplete counseling," and "language expression." Additionally, we incorporated the doctor's title as a covariate in the model to examine how specific topics varied across different conditions. The results indicated that higher-titled doctors were more likely to receive complaints about the cost of treatment and whether the question was answered, whereas lower-titled doctors were more likely to receive complaints related to physician's knowledge, incomplete counseling, and response speed. This study not only enhances our understanding of mobile health services but also provides targeted insights for healthcare providers to improve their services, thereby contributing to the advancement of patient-centered care.

Exploration of the nonlinear relationship between social support and the establishment of long-term doctor-patient relationships: An empirical analysis based on virtual doctor teams

BACKGROUND

The continued development of information technology has led to the emergence of online medical consultation platforms. Online virtual doctor teams, a new model in online medical care, have received significant attention for their ability to provide increased social support to patients. Many scholars have explored the role of social support in doctor-patient communication, usually focusing on a linear relationship between the impact of social support on medical outcomes. In the present study, we will explore the existence of a nonlinear relationship between the two.

METHODS

In the present study, we use doctor teams from a leading online consultation platform in China--Haodf online (https://www.Haodf.com), as our research object. In total, 610 doctor teams and 413,778 consultation records spanning from June 2017 to November 2019 are collected and used to explore how social support supplied by doctor teams during interactive communication would affect the establishment of long-term doctor-patient relationships. We also explore the moderating role of team leadership type in this process. From the perspective of social support theory, we select representative factors of informational support and emotional support provided by doctor teams, namely, medical term use and emotional expression. We use text and sentiment analysis methods to extract social support contained in the texts of online doctor team-patient interactions and classify doctor teams into strong and weak leadership types based on leader-member status distance. Further, we used a logistic regression model to empirically analyze the nonlinear relationship between social support and long-term doctor-patient relationship establishment and the moderating effects of team leadership types in this process.

RESULTS

The present results show that inverted U-shaped relationships exist among medical term use, emotional expression, and long-term doctor-patient relationship establishment, respectively. Doctor teams with strong leadership type make the inverted U-shaped curve between medical term use and long-term doctor-patient relationship establishment flatter than teams with weak leadership type.

CONCLUSION

In the present study, we enrich the application of social support theory in the field of online health consultation and provide suggestions for how different types of online doctor teams provide social support to patients.

How Do Team-Level and Individual-Level Linguistic Styles Affect Patients' Emotional Well-Being-Evidence from Online Doctor Teams

BACKGROUND

In the post-epidemic era, online medical care is developing rapidly, and online doctor teams are attracting attention as a high-quality online medical service model that can provide more social support for patients.

METHODS

Using online doctor teams on the Haodf.com platform as the research subject, this study investigates the key factors in the process of doctor-patient communication, which affects patients' emotional well-being. We also explore the different roles played by doctors as leaders and non-leaders in doctor-patient communication. From the perspective of language style, we select representative factors in the process of doctor-patient communication, namely the richness of health vocabulary, the expression of emotions, and the use of health-related terms (including perceptual words and biological words). We extract both team-level and individual-level linguistic communication styles through textual and sentiment analysis methods and empirically analyze their effects on patients' emotional well-being using multiple linear regression models.

RESULTS

The results show that the expression of positive emotions by the team and attention to patients' perceptions and biological conditions benefit patients' emotional well-being. Leaders should focus on the emotional expression, whereas non-leaders should focus on the use of perceptual and biological words.

CONCLUSIONS

This study expands the application of linguistic styles in the medical field and provides a practical basis for improving patients' emotional well-being.

Online selection of a physician by patients: the impression formation perspective

Background

With the rapid development of online health communities (OHCs), an increasing number of physicians provide services in OHCs that enable patients to consult online in China. However, it is difficult for patients to figure out the professional level of doctors before consultation and diagnosis because of information asymmetry. A wealth of information about physicians is displayed in their profiles as a new way to help patients evaluate and select quickly and accurately.

Objective

This research explores how the profile information (PI) presented in OHCs influences patients' impression formation, especially the perception of professional capital (i.e., status capital and decisional capital). The impression influences their intention to consult further, which is partially mediated by the initial trust. The Toulmin’s model of argumentation is used to decide the strength of the argument presented in physicians’ homepage information and divide it into claim, data, and backing.

Methods

This study conducts an internet experiment and recruits 386 subjects through the internet to investigate the effect of impression formation on online selection behavior by a patient.

Results

The results show that the strength of argument has a significant positive association with the perception of professional capital. Perceptions of professional capital are highest when a fully composed argument (claim/data/backing) is included in a profile, with claim/data being the next highest and claim only the lowest. Recommendations from connections have the strongest impact. In turn, patients' selection decisions are influenced by their perception of professional capital, which is partially mediated by initial trust.

Conclusions

This study is significant in terms of its implications for theory and practice. On the one hand, this research contributes to the online health community literature and suggests that the perception of professional capital on physicians should be pre-presumed and built based on the information before in-person interaction online. On the other hand, this study is helpful in understanding the effect of various components included in PI on perceiving physicians’ abilities, and not all information is equally important.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01936-0.

Promotion strategy for online healthcare platform during the COVID-19 pandemic: Evidence from Spring Rain Doctor in China

INTRODUCTION

Online healthcare platform (OHP) is a new form of medical treatment that solves the problems of an unbalanced distribution of medical resources in China. Especially during the COVID-19 pandemic, OHP has greatly reduced the medical pressure of the hospital and the risk of cross-infection.

METHODS

Based on self-determination theory (SDT) (Ryan and Deci, 2000), privacy calculus theory (PCT) (Culnan, 1999) and perceived value theory (PVT) (Choi, 2004), this study uses evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the promotion of OHP. Moreover, we conduct numerical simulations with the help of program compilation.

RESULTS

The results demonstrate that (1) both the qualification inspection of doctors and the investment in information protection influence doctors' participation in and patients' usage of OHP; (2) both the initial probabilities of doctor participation and patient usage influence the multi-game results; (3) the trend of doctors joining OHP is affected by registration cost, time cost, and reputation loss; and (4) the trend of patients using online healthcare is mainly decided by the cost.

CONCLUSION

This study takes the Spring Rain Doctor as an example to verify the game results. To further popularize online medical treatment among patients, the platform should attach importance to the inspection of doctors and the protection of privacy information and strengthen its publicity in remote places.

The role of individual service and team-based service price in the online environment: A view from the price difference

Different from the traditional medical market, the online medical market allows physicians considerable discretion in setting prices of their services, which is beginning to be paid close attention to. Physicians face a challenge with the introduction of various service styles. Guided by transaction utility theory and price fairness, this study aims to investigate the influence of pricing strategy on service demands from the price difference perspective by focusing on two typical service models: individual service and team-based service. Moreover, team characteristics (response speed and team size) are also considered. The data collection was done in March 2018 and repeated in May 2018, and physicians who provide both individual service and team-based services are included in our study. Finally, a dataset consisting of 1,100 teams with 1,100 physician leaders from 14 departments such as obstetrics and gynecology department were collected from an online medical platform in China. Empirical results support most of our hypotheses. A negative influence of team-based price was observed. As a substitute service, a higher individual service price will make patients turn to team-based service. Moreover, individual service prices negatively moderated the relationship between team-based service prices and demands. By calculating the price difference between the individual service price and the team-based service price, we found a negative role of the price difference affecting patient purchase decisions. Although we did not find a significant effect of team size, a quick response can attract more patients. Price fairness provides a proper framework for understanding pricing strategy in individual and team-based service in an online environment. Understanding the effects of prices from a price difference perspective has both theoretical and practical contributions. Specifically, this study contributes to knowledge on price fairness, online medical platforms, and virtual teams, and provides management suggestions.

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.

Why online medical teams disband? The role of team diversity and leadership type

Purpose

Online medical teams (MTs), involving collaboration between remote healthcare workers, can provide comprehensive and rapid healthcare to patients. The growth in MTs is continuing, with popularity growing among doctors and patients, but some MTs disband, which could break the continuity of healthcare services provided. We aim to address this pressing issue by exploring the effects of team diversity and leadership types on team status (i.e. team disbandment (TD)). This paper systematically investigates the influences of team diversity, including separation, variety and disparity diversity and the effects of leadership types, including strong, equal and weak types.

Design/methodology/approach

A data set consisting 1,071 online MTs was collected from the Good Doctor website, a leading Chinese online health community (OHC), on January 10, 2018. The data captured included 206 teams which disbanded after 3 months collaboration. Logistic regression and maximum likelihood estimation (MLE) were used to examine their effects.

Findings

The results show that variety diversity, related to departments, positively affects TD, but disparity diversity, referring to clinician titles, negatively affects TD. Separation diversity, in terms of team member attitudes, exerts a negligible influence on disbandment. Although strong and equal leadership types negatively influence TD, they are seen to strengthen the positive effect of variety diversity, suggesting stable structure combinations of strong or equal-type leadership and low department diversity, as well as the match of weak-type leadership and high department diversity.

Originality/value

This paper extends the current understanding of virtual teams and OHCs by examining the role of leadership types and team diversity, and their influencing role on team status. The pairwise combinations are obtained to effectively reduce the disbandment probability of medical teams operating in OHCs, which could help platform managers, team founders and those connected with MTs deal with the team-disbandment crisis, providing both theoretical and practical implications to healthcare providers and researchers alike.

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.