Online Health Platforms Research Articles

Understanding online dual practice of public hospital doctors in China: a mixed-methods study

Telemedicine and telehealth hold promise for reducing access barriers, improving quality and containing medical costs. As Internet companies enter the healthcare market, a rising number of online healthcare platforms have emerged worldwide. In some countries like China, public hospital doctors are providing direct-to-consumer telemedicine services on these commercial platforms as independent providers. Such online service provision creates a new form of dual practice, which we refer to as 'online dual practice' in this study. Using a mixed-methods design, this study aims to investigate the prevalence of online dual practice, doctors' time allocation and motivations for engaging in it and its potential impacts on the health system in China. We use the web-crawled data from four leading online health platforms to examine the prevalence of online dual practice in China. Then we conduct in-depth interviews with 38 active doctors on these platforms to investigate their time allocation, motivations and perception regarding online service provision. We find that the nationwide prevalence of online dual practice in China reaches at least 16.5% in 2020 and that it is more common among senior public hospital doctors. Public hospital doctors mainly use small pockets of time during working hours and after-hours to render services on the platforms The five most commonly cited motivations for their engagement in online dual practice are efficiency improvement, personal control, career development, financial rewards and serving the patients. Interviewed doctors believe that their online service provision is conducive to increasing healthcare access and improving efficiency, but some also express their concerns about the quality of care. Further analysis shows that the impact of online dual practice on health system performance remains an open question and regulatory policies on it should be health-system specific.

Which voice are you satisfied with? Understanding the physician–patient voice interactions on online health platforms

Patients are usually sensitive to physicians' attitude and behavior when physicians make decisions in patients' consultations. However, the consequences of physicians' interactive voices on patients' perception have rarely been explored. This study develops a multistage online interaction model to explore the effects of the key vocal emotional cues of physician voice on patient-perceived satisfaction. We collect data of over 44,000 voice interactions regarding lung disease from 889 physicians on a leading online health platform. By using the theoretical lens of emotional contagion, we find that the voice pitch and intensity of physicians, respectively, positively and negatively affect patient-perceived satisfaction, in turn positively affecting physician economic returns in the postinteraction stage. However, the negative effect of intensity is low for physicians with high recommendation popularity (a factor in the preinteraction stage), suggesting that physician-inherent information has an anchoring effect. These findings not only provide new insights into the literature on online physician–patient interaction by identifying the novel nonverbal cues of physician voice, their consequent effects, and boundary conditions, but also provide decision support for physicians and platform managers.

Exploring the switching intention of patients to e-health consultations platforms: blending inertia with push–pull–mooring framework

PurposeTechnology has revolutionized the delivery of health-care services, with e-consultations becoming popular mode of service delivery, especially during the pandemic. Extant research has examined the adoption of e-health consultation services, with little attention paid to examine the switching behavior. This study aims to identify factors affecting patients’ intentions to switch from conventional mode i.e. visiting hospitals/clinics to e-health consultations.Design/methodology/approachTo understand this we use the push–pull–mooring (PPM) framework and integrate variables from status quo bias framework to the model. A cross-section research design was used, which rendered 413 valid responses which were obtained from the patients visiting a traditional hospital setup. The data was analyzed using partial least square – structural equation modeling using SmartPLS 3.0.FindingsFindings suggest that push effects (inconvenience and perceived risk), pull effects (opportunity for alternatives and ubiquitous care), mooring effects (trust) and inertia significantly influence patients’ switching intentions from visiting hospitals/clinics to e-health consultations. Further, habit and switching cost positively influence inertia.Practical implicationsThis study shall enable online health-care service providers and practitioners to understand patients’ intentions to switch to online health platforms and accordingly develop related marketing strategies, services and policies to encourage them to switch to the new offerings.Originality/valueThe current study enriches the previous research on e-health services by applying and extending PPM framework as the base model and showing its efficiency in predicting individuals switching intentions in the context of emerging economies. This study bridges the gap by focusing on switching behavior in context of health services.

What Influences Physicians' Online Knowledge Sharing? A Stimulus-Response Perspective.

During the COVID-19 pandemic, online health platforms and physicians’ online knowledge sharing played an important role in public health crisis management and disease prevention. What influences physicians’ online knowledge sharing? From the psychological perspective of stimulus–response, this study aims to explore how patients’ visit and patients’ consultation influence physicians’ online knowledge sharing considering the contingent roles of physicians’ online expertise and online knowledge sharing experience. Based on 6-month panel data of 45,449 physician–month observations from an online health platform in China, the results indicate that both patients’ visit and patients’ consultation are positive related to physicians’ online knowledge sharing. Online expertise weakens the positive effect of patients’ consultation on physicians’ online knowledge sharing. Online knowledge sharing experience weakens the positive relationship between visit of patient and physicians’ online knowledge sharing, and enhances the positive relationship between patients’ consultation and physicians’ online knowledge sharing. This study contributes to the literatures about stimulus–response in psychology and knowledge sharing, and provides implications for practice.

Digital intervention increases influenza vaccination rates for people with diabetes in a decentralized randomized trial

People with diabetes (PWD) have an increased risk of developing influenza-related complications, including pneumonia, abnormal glycemic events, and hospitalization. Annual influenza vaccination is recommended for PWD, but vaccination rates are suboptimal. The study aimed to increase influenza vaccination rate in people with self-reported diabetes. This study was a prospective, 1:1 randomized controlled trial of a 6-month Digital Diabetes Intervention in U.S. adults with diabetes. The intervention group received monthly messages through an online health platform. The control group received no intervention. Difference in self-reported vaccination rates was tested using multivariable logistic regression controlling for demographics and comorbidities. The study was registered at clinicaltrials.gov: NCT03870997. A total of 10,429 participants reported influenza vaccination status (5158 intervention, mean age (±SD) = 46.8 (11.1), 78.5% female; 5271 control, Mean age (±SD) = 46.7 (11.2), 79.4% female). After a 6-month intervention, 64.2% of the intervention arm reported influenza vaccination, vers us 61.1% in the control arm (diff = 3.1, RR = 1.05, 95% CI [1.02, 1.08], p = 0.0013, number needed to treat = 33 to obtain 1 additional vaccination). Completion of one or more intervention messages was associated with up to an 8% increase in vaccination rate (OR 1.27, 95% CI [1.17, 1.38], p < 0.0001). The intervention improved influenza vaccination rates in PWD, suggesting that leveraging new technology to deliver knowledge and information can improve influenza vaccination rates in high-risk populations to reduce public health burden of influenza. Rapid cycle innovation could maximize the effects of these digital interventions in the future with other populations and vaccines.

Are Diverse Media Better than a Single Medium? The Relationship between Mixed Media and Perceived Effect from the Perspective of Online Psychological Counseling.

The progress of new media has promoted the development of online health consultations. Previous research has investigated the impact of media richness on user satisfaction; however, little attention has been given to the mixed effects of the nesting of multiple media. The purpose of this study is to analyze the impact and differences of the use of single or mixed media on users’ perceived effect from the perspectives of social support and satisfaction by mining user reviews on online health platforms. The data were collected from a professional online psychological counseling platform. We collected data on 48,807 reviews from 11,694 users. Text annotation and sentiment analysis were then used to extract variable eigenvalues from the reviews. One-way analysis of variance (ANOVA) and hierarchical regression analysis were used for statistical analysis. The results show that mixed media with different richness has a significant impact on the users’ perceived effects. Among them, compared to “text + audio,” using “text + audio + video/face to face” can significantly improve the users’ perceived social support and satisfaction. However, compared to single medium, mixed media with higher richness may not necessarily achieve a better effect. We found that the inclusion of “video/face to face” mixed media significantly reduced the users’ perceived social support and satisfaction compared to text or audio use alone. These research results complement the blank media richness theory in the field of online health care and provide guidance for improving the personalized customization of online psychological counseling platforms.

Two-stage matching decision-making method in medical service supply chain

ABSTRACT Medical service matching between suppliers and demanders should be emphasised. The supply chain promotes the development of practical matching decision-making methods. However, little is known about the general approaches used to explore matching problems in the context of medical service supply chain management. We put forward an effective two-stage matching decision-making method, including a grouping model and a matching model, that considers patient similarity and bilateral preferences. The grouping model is constructed based on the similarity matrix of patients. Next, specific rules are employed to calculate the integrated preference difference values between patients and doctors. The two-stage matching method considering bilateral preferences is then presented. Finally, a case study is conducted on an online health platform to verify the proposed method.

Online health behavior: Antecedents and outcomes of employee participation in an organization's online health program

AbstractWorking in organizations can result in health problems for employees, who may develop health conditions such as hypertension, cardiac problems, and obesity. To promote healthy behavior among employees, some companies use tailored online health platforms, which are customizable and accessible. Although these programs are available to all employees, they differ in terms of their participation, and we do not understand the reasons underlying variations in the use of these programs. We also have a limited understanding of the potential organizational outcomes of such participation. Thus, in this study, we develop a model of the antecedents and outcomes of employees’ participation in an organization's online health program. We propose that employees’ health beliefs, social influence to participate, and intention to participate influence their participation in an organization's online health program, which is associated with their health behavior within the organization. In turn, employees’ organizational health behavior influences organizationally relevant outcomes through mediation by availability of task‐ and family‐related support from coworkers. We tested our model through a four‐phase, multisource study by using data from 331 employees working in an Indian information technology company. Results generally supported our model. Our findings contribute to our knowledge of employees’ health behavior by examining why they may participate in online health programs and how their participation may promote health behavior and lead to organizationally relevant outcomes.

Exploring Doctors' Willingness to Provide Online Counseling Services: The Roles of Motivations and Costs.

With the impetus of information communication technology (ICT), emerging eHealth has attracted increasing number of doctors’ participation in online health platforms, which provide various potential benefits to doctors. However, previous studies on eHealth have seldom distinguished different service modes provided by doctors. In addition, the bulk of the literature has considered doctors’ motivations based solely on online environments. To fill this gap, this study combines expectancy theory and the Bagozzi, Dholakia, and Basuroy (BDB) model to examine the relationships between anticipated outcomes, performance expectations, and effort intentions from online and offline perspectives. Doctors’ behavioral intentions are further divided into two categories: the willingness to offer free services and paid services. Using SmartPLS, this study conducts structural equation modeling (SEM) to analyze 311 sample data. The results show that extrinsic motivations (i.e., extrinsic rewards, expected relationships, and image) and intrinsic motivation (i.e., a sense of self-worth) significantly influence the desire to serve patients well, which in turn positively affects the willingness to offer free services and the willingness to offer paid services. Moreover, counseling time is confirmed as the main cost, which negatively moderates the relationships between desire and behavioral intentions. The findings provide theoretical insights for eHealth and provide practical suggestions to develop marketing strategies for online health platform providers.

Future of information technology and telecommunication in type 1 diabetes clinical care: results of an online survey

ObjectiveTo assess the attitude of people living with type 1 diabetes toward the use of information and communication technology (ICT) to facilitate access to diabetes healthcare professionals (HCPs).Research design and...

What motivates physicians to share free health information on online health platforms?

Online platforms enable physicians to share health and medical information with the public; however, little research has been conducted to explore why physicians share free health education information. Drawing on motivation theory, this study develops a theoretical model to explore the influences of material and professional motivation on free information sharing and the contingent roles of professional expertise and online expertise. The model is tested using a six-month panel data set of 61,326 physicians’ sharing experiences. The results indicate that in addition to material motivation, professional motivation also plays a primary role in inducing physicians to share free information. However, when a physician's professional and online expertise is at a high level, the effect of material motivation is weakened and professional motivation plays a more important role. This study contributes to the literature on knowledge sharing, online health behavior, and motivation theory, and provides implications for practice.

Why Do Patients Move from Online Health Platforms to Hospitals? The Perspectives of Fairness Theory and Brand Extension Theory.

Online healthcare services are growing rapidly. Patients can receive health support through communication with online health professionals. However, previous research on eHealth has focused on patients’ online behavioral intentions. The purpose of this study is to investigate the effect of online patient–doctor communication on offline behavioral intentions and to propose a conceptual model for eHealth. We integrate fairness theory and band extension theory to develop a research model. This is further tested by structural equation modeling (SEM), with 279 valid sets of data from patients on the online health platform. Using partial least squares analysis (PLS), the results show that perceived fairness is an important factor affecting online satisfaction and the willingness to ask online doctors, which in turn has a positive impact on the willingness to go to offline doctors. These findings shed light on the research model for eHealth and offer suggestions for improving patients’ online satisfaction, as well as both online and offline behavioral intentions.

Identifying Brand Cheerleaders in an Electronic Health Platform

Online health platforms have become an important component of the preventative care ecosystem, and referral systems are potentially powerful instruments of growth for these platforms. These referral systems ultimately depend on how effective the existing users are able to recruit new users. To provide some insights about how effective recruiters can be identified, we study unique data from a large online health platform that has a “invite your friends” program. Because we are able to see each instance that a user sends out an invitation, regardless of whether it was accepted or not, this information can be used in establishing partial identification for invitation acceptance regions across user types. Our inferred bounds suggest that consumer activation with the online platform may be informative at predicting invitation acceptance, as users who are engaged with the platform achieve greater success at recruiting new users.

Understanding the promises and premises of online health platforms

This article investigates the claims and complexities involved in the platform-based economics of health and fitness apps. We examine a double-edged logic inscribed in these platforms, promising to offer personal solutions to medical problems while also contributing to the public good. On the one hand, online platforms serve as personalized data-driven services to their customers. On the other hand, they allegedly serve public interests, such as medical research or health education. In doing so, many apps employ a diffuse discourse, hinging on terms like “sharing,” “open,” and “reuse” when they talk about data extraction and distribution. The analytical approach we adopt in this article is situated at the nexus of science and technology studies, political economy, and the sociology of health and illness. The analysis concentrates on two aspects: datafication (the use and reuse of data) and commodification (a platform’s deployment of governance and business models). We apply these analytical categories to three specific platforms: 23andMe, PatientsLikeMe, and Parkinson mPower. The last section will connect these individual examples to the wider implications of health apps’ data flows, governance policies, and business models. Regulatory bodies commonly focus on the (medical) safety and security of apps, but pay scarce attention to health apps’ techno-economic governance. Who owns user-generated health data and who gets to benefit? We argue that it is important to reflect on the societal implications of health data markets. Governments have the duty to provide conceptual clarity in the grand narrative of transforming health care and health research.

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

BackgroundMany Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers.ObjectiveThe aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information.MethodsWe conducted two-fold analyses on the data collected from the “Good Doctor Online” platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits.ResultsOn the “Good Doctor Online” platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors’ technical skills and bedside manner, general appreciation from patients, and description of various symptoms.ConclusionsTo the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China’s health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.