Impact of the Price of Gifts From Patients on Physicians' Service Quality in Online Consultations: Empirical Study Based on Social Exchange Theory

Consulting doctors online after offline treatment: investigating the effects of online information on patients' effective use of online follow-up services

Introduction

The use of online follow-up services (OFUS) is becoming an increasingly important supplement to hospital care. Through OFUS, patients can find their doctors in online health communities (OHCs) and receive remote medical follow-ups after hospital treatment. However, the rate of effective use of OFUS by current patients is still low, and there is an urgent need for research to investigate the online information factors that affect patients' effective use of OFUS.

Methods

Based on the elaboration likelihood model (ELM) of persuasion and an analysis of a panel dataset including 3,672 doctors in a leading OHC in China, this study explores how online information from doctors' knowledge contributions and patient feedback influences patients' effective use of OFUS.

Results

The results show that both doctors' knowledge contributions and patient feedback positively influence patients' effective use of OFUS. Doctors' paid knowledge contributions and patients' paid feedback have stronger persuasive effects than doctors' free knowledge contributions and patients' free feedback, respectively. Moreover, there is a substitutional relationship between doctors' paid and free knowledge contributions and between patients' paid and free feedback in influencing patients' effective use of OFUS.

Discussion

The findings of this study suggest that OHC platforms and healthcare providers should account not only for the persuasive effects of doctors' knowledge contributions and patient feedback but also for influential differences and relationships between the types of doctors' knowledge contributions and patient feedback to better persuade patients to effectively use OFUS.

Exploring the Effects of Online Physician Voice Pitch Range and Filled Pauses on Patient Satisfaction in Mobile Health Communication

The convenience of mobile devices has driven the widespread use of voice technology in mobile health communication, significantly improving the timeliness of online service. However, the issue of listening to therapeutic content, which requires great cognitive effort and may exceed the patient's information processing capacity (i.e., information overload), is of concern. Based on information processing theory, this study reports how online physicians' voice characteristics (pitch range and filled pauses) affect patient satisfaction. We obtained 10,585 mobile voice consultation records of 1,416 doctors from China's largest mHealth platform and analyzed them using audio mining and empirical methods. Results showed that pitch range (β = 0.0539, p < .01) and filled pauses (β = 0.0365, p < .01) in doctors' voice positively influenced online patient satisfaction. However, the effect of filled pauses becomes weaker for patients with higher health literacy and higher disease risk. This suggests that there is heterogeneity in the way different patients process audio information. This study provides important insights for guiding online physician behaviors, enhancing patient satisfaction, and improving mobile health platform management.

Physicians' Online Writing Language Style and Patient Satisfaction: The Mediator of Depth of Physician-Patient Interactions

Online health counseling (OHC) is increasingly important in modern healthcare. This development has attracted considerable attention from researchers. However, the reality of the lack of physician-patient communication and dissatisfaction with online health services remains prevalent, and more research is needed to raise awareness about important issues related to OHC services, especially in terms of patient satisfaction and depth of interaction (i.e., the product of the number of interactions and the relevance of the content). This study constructs an empirical model to explore the relationship between physicians' online writing language style (inclusive language and emojis), depth of physician-patient interactions, and patient satisfaction. The study obtained 5064 online health counseling records from 337 pediatricians and analyzed them using text mining and empirical methods. The results showed that physicians' inclusive language (β = 0.3198, p < 0.05) and emojis (β = 0.6059, p < 0.01) had a positive impact on patient satisfaction. In addition, the depth of the physician-patient interaction partially mediated this effect. This study promotes a better understanding of the mechanisms of physician-patient interactions in online settings and has important implications for how online physicians and platforms can better provide online healthcare services.

Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic

This study investigates the determinants of service satisfaction with online healthcare platforms using machine learning (ML) algorithms. By training and testing eleven ML models based on data mined from a leading online healthcare platform in China, we obtained the best-performing ML algorithm for service satisfaction prediction, namely, Light Gradient Boosting Machine. Furthermore, our empirical results indicate that gifts, patient votes, popularity, fee-based consultation volume, gender, and thank-you letters positively impact service satisfaction, while the impacts of consultation volume, free consultation volume, views, waiting time, articles, physician title, and hospital level are negative. We discuss the theoretical and managerial implications.

Why should I consult? The impact of social support on patient consultation in online healthcare communities

The COVID-19 epidemic put the traditional healthcare system and offline consultation method under strain. Patient consultations through online healthcare communities (OHCs) provide patients and physicians with a more convenient and secure route. Based on the social support theory, this study explores the impact of three dimensions of social support from physicians—information diagnosticity, source credibility, and emotional support—on patient consultations in OHCs and their moderating effect on patients’ compliments. We utilized Python Spiders to retrieve data from Haodf.com and gathered 2,982 physician reports. The model uses OLS regression with fixed effect estimations. The results show that these three dimensions of social support are positively impacted by consultation. Furthermore, patients’ compliments weaken the positive relationship between the three dimensions of physicians’ social support and patient consultations. This study contributes to the literature on social support theory in OHCs by exploring the physicians’ social support dimension and its impact on patient consultation. Moreover, this study makes practical contributions to physicians and platform administrators in OHCs.

A Research Based on Online Medical Platform: The Influence of Strong and Weak Ties Information on Patients’ Consultation Behavior

As an indispensable part of contemporary medical services, Internet-based medical platforms can provide patients with a full range of multi-disciplinary and multi-modal treatment services. Along with the emergence of many healthcare influencers and the increasing connection between online and offline consultations, the operation of individual physicians and their teams on Internet-based medical platforms has started to attract a lot of attention. The purpose of this paper is to, based on an Internet platform, study how the information on physicians’ homepages influences patients’ consultation behavior, so as to provide suggestions for the construction of physicians’ personal websites. We distinguish variables into strong- and weak-ties types, dependent on whether deep social interactions between physicians and patients have happened. If there exist further social interactions, we define the variable as the “strong ties” type, otherwise, “weak ties”. The patients’ consultation behavior will be expressed as the volume of online consultation, i.e., the number of patients. We obtained the strong and weak ties information of each physician based on EWM (entropy weight method), so as to establish a regression model with explained variable, i.e., the number of patients, and three explanatory variables, i.e., the strong and weak ties information, and their interaction term. The estimation results verified our hypotheses and proved to be robust. It showed that both strong and weak ties information can positively influence patients’ consultation behavior, and the influence of weak ties information is greater. Regarding the positive influence of strong and weak ties, we found a trade off effect between them. Based on the results, we finalize with some suggestions on how to improve a physician’s online medical consultation volume.

Factors influencing online orthopedic doctor-patient consultations

BACKGROUND

Online doctor-patient consultation is a new option for orthopedic patients in China to obtain a diagnosis and treatment advice. This study explores the factors associated with online consultation to formulate operational guidelines for managing online consultations in an online medical community (OMC).

METHODS

An empirical model was developed to identify the factors that influence online orthopedic doctor-patient consultations in an OMC while focusing on the perceived value of and perceived trust in online consultations. The moderating effects of different risk categories of orthopedic diseases were also considered. Data from 339 feedback surveys from orthopedic patients who used online consultation services and Stata software version 14.0 were used to estimate the model parameters and test the robustness of the empirical model.

RESULTS

Of those who completed the feedback surveys, 53.42% were female patients, 82.27% were between 18 and 60 years old, and 61.98% sought consultations online more than 2 times per year. Model analysis demonstrated that the regression coefficients of the perceived value of and perceived trust in online consultations are 0.489 (p < 0.01) and 0.505 (p < 0.01), respectively. The interaction coefficient between disease risk and perceived value is 0.336 (p < 0.01), and the interaction coefficient between disease risk and perceived trust is - 0.389 (p < 0.01).

CONCLUSIONS

Orthopedic patients' perceived value of and perceived trust in online consultations in an OMC can significantly influence their intention to seek online disease diagnosis and treatment consultations. The effects of perceived value and perceived trust on patients' intention to consult vary significantly across different disease risk categories. Therefore, enhancing the perceived value and perceived trust of orthopedic patients is an important component of OMC operation and management.