Online Healthcare Platforms Research Articles

Competition and coordination between traditional and internet hospitals

AbstractMotivated by the recent emergence and rapid growth of online healthcare platforms, this paper investigates its impacts on the healthcare ecosystem by taking into consideration of heterogeneous patients, homogeneous service providers and profit-driven hospitals. A game-theoretical model with a queueing framework is developed, and two healthcare systems are considered, including a monopolistic system with either an offline, traditional hospital, or an online, Internet hospital, and a duopolistic system with both kinds of hospitals. Our study finds that in both systems a large population of patients always increases the healthcare supply and demand and the hospital utilization, which leads to an improvement not only in the hospital profitability, but also in the welfare of both patients and service providers. When considering a monopolistic system only, our study suggests that the monopolistic Internet hospital outperforms its counterpart in hospital utilization only when the wait cost is not high (i.e., patients are less sensitive to wait time), especially when the wait cost is medium and the patient-provider ratio is small. When considering a duopolistic system, this study concludes that the decisions of patients, service providers, and hospital managers of both hospitals are interrelated, and that a large population of service providers increases its hospital’s supply, demand, and wage rate, while decreases its wait times. When comparing the two monopolistic with the duopolistic system, the latter outperforms and improves every entity’s utility (i.e., patients, hospital, and society) when there is abundance of service providers with more being allocated to the Internet hospital, whereas the monopolistic system dominates when the patient population is large, and more providers are allocated to the traditional hospital.

Foot in both camps: How do activities on third-party online healthcare platforms affect doctors' demand on official online healthcare platforms?

Using empirical data from a third-party platform and a comprehensive public hospital (equipped with an official online healthcare platform) in China, this study employs a two-stage Heckman selection model and find that third-party online healthcare platforms (OHPs) should not be considered an obstacle to promoting official OHPs. Instead, doctors' activities on third-party OHPs increase the demand for doctors on official OHPs. Moreover, this study explores the heterogeneity in the effects of the doctor groups. For example, the impact of specific efforts is stronger for doctors with higher professional titles but weaker for doctors with higher online ratings. This study provides valuable insights for policymakers and hospital administrators to promote and coordinate online services across multiple platforms.

Continuous adoption of online healthcare platforms: an extension to the expectation confirmation model and network externalities

BackgroundOnline healthcare platforms (OHPs) promote the accessibility of health services while integrating online and offline service delivery. Continuous adoption of these platforms can reduce the information asymmetry between patients and healthcare providers. However, existing study has rarely focused on how the network externalities of these platforms influence patients’ continuous adoption. Expectation Confirmation Model (ECM) explains the continuous behavior after initial adoption and reveal the factors influencing satisfaction and continuous adoption intention. Few studies have integrated network externalities with the ECM to understand patients’ continuous adoption of OHPs.ObjectiveThis study aims to explore what factors affect the continuous adoption of OHPs based on an extension to the ECM and network externalities. We propose a comprehensive model for the continuous adoption of OHPs by patients with chronic diseases. We extend the conceptual framework by incorporating constructs related to perceived value, habits, and switching costs.MethodsThis study utilized a web-based survey to collect data from participants in China. We selected a sample of individuals who had experience with OHPs using a random sampling method. This method ensured that participants was randomly chosen from a pool of patients without any regional bias. A total of 568 questionnaires were collected and 518 valid questionnaires were obtained. Structural equation modeling was applied to assess the relationships among the constructs. Hierarchical regression analysis was utilized to examine the moderating effects, while the mediating effects were investigated using the bootstrapping approach.ResultsDirect network externality had significant positive impacts on the confirmation (β = 0.232, p < 0.001) and perceived value (β = 0.167, p < 0.010). Cross network externality had significant positive impacts on confirmation (β = 0.307, p < 0.001) and perceived value (β = 0.120, p < 0.05). Indirect network externality had significant positive impacts on confirmation (β = 0.169, p < 0.010) and perceived value (β = 0.270, p < 0.001). Confirmation had a significant positive impact on perceived value (β = 0.205, p < 0.001) and satisfaction (β = 0.508, p < 0.001). In addition, satisfaction had a significant positive impact on continuous adoption intention (β = 0.579, p < 0.001). Continuous adoption intention had a significant positive impact on continuous adoption behavior (β = 0.547, p < 0.001). Confirmation and perceived value significantly mediate the relationship between network externalities and satisfaction. Moreover, both confirmation and perceived value enhance continuous adoption intention by positively influencing satisfaction, which serves as a mediator. Additionally, confirmation directly influences satisfaction through the mediating role of perceived value. Habits and switching costs positively moderate the relationship between continuous adoption intention and behavior.ConclusionsThis study contributes by expanding the incorporation of network externalities into the ECM. Results enrich the existing literature on the continuous adoption of professional online platforms.

The faster or richer the response, the better performance? An empirical analysis of online healthcare platforms from a competitive perspective

The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In this light, we extract medical service-related information from physicians' response behavioral data on a prominent healthcare platform, and empirically investigate the factors affecting physicians' online performance from a competitive perspective as well as the gender differences in these effects. The results indicate that physicians' responses significantly impact their online performance, revealing a competitive relationship between physicians and their colleagues in the same department. Specifically, a fast response time and informative responses are positively correlated with the focal physician's performance, whereas colleagues' informative responses negatively impact the focal physician's performance, and this relationship is mediated by the focal physician's response informativeness. Nevertheless, there is no significant correlation between colleagues' response time and the focal physician's performance. The results also unveil that gender moderates the effect of response informativeness on the focal physician's performance. Specifically, colleagues' response informativeness has a more significant impact on male physicians' performance than on female physicians' performance, suggesting a greater propensity for competition among male physicians. Our findings could offer decision support for enhancing physician performance and healthcare platform management.

An Effective Doctor Recommendation Algorithm for Online Healthcare Platforms

The emergence of online healthcare platforms provides patients with convenience, but choosing the right doctor among the thousands of doctors available on these platforms has become a challenge for patients. The majority of these platforms recommend the same doctors to all patients based on a global ranking, disregarding individual patient preferences. The use of recommender systems helps to resolve this issue by assisting patients in locating doctors who meet their preferences and requirements. Particularly, Collaborative Filtering (CF) algorithms have been extensively utilized to generate personalized recommendations for a variety of applications. Despite their success, they still need to be further optimized to address both the sparsity and cold-start problems due to insufficient data. In this paper, we propose an effective doctor recommendation approach to assist patients in searching for satisfactory doctors who precisely match their preferences regardless of time and location. The proposed approach employs Multi-Criteria CF and content filtering to enhance the quality of recommendations by mitigating the impact of data sparsity and cold start challenges. Offline tests conducted on a real-world dataset show that the proposed approach is superior to state-of-the-art approaches in addressing the aforementioned issues and boosting prediction accuracy and coverage.

How to detect fake online physician reviews: A deep learning approach.

The COVID-19 pandemic has spurred an increased interest in online healthcare and a surge in usage of online healthcare platforms, leading to a proliferation of user-generated online physician reviews. Yet, distinguishing between genuine and fake reviews poses a significant challenge. This study aims to address the challenges delineated above by developing a reliable and effective fake review detection model leveraging deep learning approaches based on a fake review dataset tailored to the context of Chinese online medical platforms. Inspired by prior research, this paper adopts a crowdsourcing approach to assemble the fake review dataset for Chinese online medical platforms. To develop the fake review detection models, classical machine learning models, along with deep learning models such as Convolutional Neural Network and Bidirectional Encoder Representations from Transformers, were applied. Our experimental deep learning model exhibited superior performance in identifying fake reviews on online medical platforms, achieving a precision of 98.36% and an F2-Score of 97.97%. Compared to the traditional machine learning models (i.e., logistic regression, support vector machine, random forest, ridge regression), this represents an 8.16% enhancement in precision and a 7.7% increase in F2-Score. Overall, this study provides a valuable contribution toward the development of an effective fake physician review detection model for online medical platforms.

Factors influencing engagement in online dual practice by public hospital doctors in three large cities: A mixed-methods study in China.

In the digital age, a rising number of public sector doctors are providing private telemedicine and telehealth services on online health care platforms. This novel practice pattern - termed online dual practice - may profoundly impact health system performance in both developed and developing countries. This study aims to understand the factors influencing doctors' engagement in online dual practice. Using a mixed-methods design, this study concurrently collects quantitative demographic and practice data (n = 71 944) and semi-structured interview data (n = 32) on secondary and tertiary public hospital doctors in three large Chinese cities: Beijing, Shanghai and Guangzhou. We use the quantitative data to examine the prevalence of the online dual practice and its associated factors via the binary logit regression model. The qualitative data are used to further explore associated factors of online dual practice via thematic analysis. The findings about associated factors from the two parts were merged using the categories of personal, professional, and organisational characteristics. Our quantitative analysis shows that at least 47.1% of public hospital doctors are involved in online dual practice. The shares in Beijing, Shanghai, and Guangzhou are 43.7%, 53.1%, and 44.8%, respectively. This practice is more prevalent among doctors who are male, senior, and non-managerial. Different specialties, hospital ownership, hospital levels, and locations are also significantly associated with this practice. The qualitative analysis further suggests that financial returns, perceived effectiveness of telemedicine, and hospital directors' attitude towards telemedicine may affect doctors' engagement with online dual practice. Online dual practice is prevalent among doctors at tertiary and secondary public hospitals in Beijing, Shanghai, and Guangzhou. Personal, professional, and organisational characteristics are all associated with doctors' choice to engage in online dual practice. The findings in this study provide implications for promoting telemedicine adoption and developing relevant regulatory policies in China and other countries.

Nephrology workforce in China: describing current status and evaluating the optimal capacity based on real-world data

ObjectiveThis study aims to characterize the current status of the nephrology workforce in China and evaluate its optimal capacity based on real-world patient mobility data.MethodsData on nephrologists in China were collected from two prominent online healthcare platforms using web crawlers and natural language processing techniques. Hospitalization records of patients with chronic kidney disease (CKD) from January 2014 to December 2018 were extracted from a national administrative database in China. City-level paths of patient mobility were identified. Effects of nephrology workforce on patient mobility were analyzed using multivariate Poisson regression models.ResultsAltogether 9.13 nephrologists per million population (pmp) were in practice, with substantial city-level variations ranging from 0.16 to 88.79. The ratio of nephrologists to the estimated CKD population was 84.57 pmp. Among 6 415 559 hospitalizations of patients with CKD, 21.3% were cross-city hospitalizations and 7441 city-level paths of patient mobility with more than five hospitalizations were identified. After making adjustment for healthcare capacity, healthcare insurance, economic status, and travel characteristics, the Poisson regression models revealed that the number of nephrologists in both the source city (incidence rate ratio [IRR] 0.99, per 1 pmp increase) and destination city (IRR 1.07, per 1 pmp increase) were independently associated with patient mobility. An IRR plateau was observed when the number of nephrologists exceeded 12 pmp in the source city, while a rapidly increasing IRR was observed beyond 20 pmp in the destination city.ConclusionsThe nephrology workforce in China exhibits significant geographic variations. Based on local healthcare needs, an optimal range of 12–20 nephrologists pmp is suggested.

Understanding Impacts of Online Dual Practice on Health System Performance: A Qualitative Study in China

ABSTRACT The outbreak of the COVID-19 pandemic has boosted the global development of online healthcare platforms. An increasing number of public hospital doctors are providing online services on private third-party healthcare platforms, creating a new form of dual practice—online dual practice. To explore the impacts of online dual practice on health system performance as well as potential policy responses, we undertook a qualitative approach that uses in-depth interviews and thematic analysis. Following a purposive sampling, we interviewed 57 Chinese respondents involved in online dual practice. We asked respondents for their opinions on the effects of online dual practice on access, efficiency, quality of care, and advice on regulatory policy. The results suggest that online dual practice can generate mixed impacts on health system performance. The benefits include improved accessibility due to increased labor supply of public hospital doctors, better remote access to high-quality services, and lower privacy concerns. It can improve efficiency and quality by optimizing patient flows, reducing repetitive tasks, and improving the continuity of care. However, the potential distraction from designated work at public hospitals, inappropriate use of virtual care, and opportunistic physician behaviors may undermine overall accessibility, efficiency, and quality. Countries should mitigate these adverse consequences via regulations that are appropriate to their healthcare system context, policy priority, and governance capacity.

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.

Does the physicians’ medical team joining behavior affect their performance on an online healthcare platform? Evidence from two quasi-experiments

With the growing interest in and use of online healthcare platforms (OHPs), online medical team (OMT) services have gradually been recognized by physicians as an important multi-channel initiative. However, only a handful of studies have explored the pros and cons of the OMT channel over the traditional individual online service channels in an OHP or examined the critical roles of physicians’ OMT joining behavior. To fill this gap, utilizing two sets of unbalanced panel data, representing high- and low-risk diseases, from 3,500 physicians in a leading OHP in China, we investigate the impacts of physicians’ OMT joining behavior on their online performance based on the trust-transfer theory. The moderating effect of physician’s team involvement is investigated based on data from a famous OHP application. Employing a combination of propensity score matching and difference-in-differences identification, the results indicate that physicians’ OMT joining behavior positively influences their online performance. Furthermore, for the physicians who deal with low-risk diseases, their OMT involvement weakens the positive relationship between their OMT joining behavior and online performance although it has the opposite impact in the case of high-risk diseases. Our subsample analyses provide further insights into how physicians’ OMT joining behavior influences their online performance based on their seniority levels. Specifically, for physicians with higher seniority levels, the team joining behavior positively influences their online performance whereas there is no significant effect for the physicians with lower seniority levels.

A doctor recommender system based on collaborative and content filtering

&lt;span lang="EN-US"&gt;The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity.&lt;/span&gt;

Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach

Choice overload is a common problem in many online settings, including healthcare. Online healthcare platforms tend to provide a large variety of behavior intervention information or programs to help individuals modify their lifestyles to improve wellness. However, having too many options can significantly increase searching cost, prevent users from discovering the truly relevant interventions, and harm users’ long-term healthcare decision-making efficiency. This motivates us to propose a personalized healthcare recommendation system to provide tailored support for individuals’ intervention participation. The proposed framework, a deep-learning and diversity-enhanced multiarmed bandit (DLDE-MAB), integrates several predictive and prescriptive analytics components to combat the unique challenges presented in the healthcare recommendation setting. It leverages online machine learning to provide adaptive and real-time support, a theory-guided diversity promotion scheme to cover multiple healthcare needs, and deep learning to further enhance dynamic context representation. Through extensive experiments, we show that the proposed framework outperforms various competing models in terms of its adaptivity to data dynamics, diversity, and uncertainty. The proposed model and evaluation results provide important implications for business intelligence and personalized, contextualized, and agile healthcare decision making.

Research on Methods to Enhance User Stickiness in Online Healthcare Platforms - A Case Study Based on Yilu

As the population grows in China, the proportion of national spending on healthcare has increased, which promoted the development of China's internet healthcare industry. However, the number of active users in China's Internet healthcare industry is not desirable. Many users still have trust problems with online medical consultations. User stickiness and usage rates have become one of the challenges of the current Internet healthcare platforms. This paper uses Ali Health as an example to examine ways in which online healthcare platforms can enhance user stickiness. By using the case study method, it is found that there are only three ways for Yilu to enhance user stickiness. From the perspective of traffic drainage, Yilu can expand its business to multiple platforms by taking advantage of the parent company's advantages and actively innovating traffic drainage methods. Emotionally resonate with users and attracts more users. From the perspective of products, Yilu uses a series of comprehensive high-quality services, and reliable products like its medicines to increase its user stickiness. From an operational point of view, Yilu's WeChat community, membership system, and points system can also help enhance their user stickiness. Through research on Yilu's method of enhancing user stickiness, it can provide a reference for other enterprises in China’s Internet medical industry to meet users' needs, continuously improve platform services, and ultimately achieve healthy industry development.

More than Demand Propelling: Load Balancing Effects of E-Prescription on Online Healthcare Platforms

More than Demand Propelling: Load Balancing Effects of E-Prescription on Online Healthcare Platforms

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

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. 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. 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. 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.

Repetitions in online doctor–patient communication: Frequency, functions, and reasons

ObjectivesTo attain insights into language repetition during online doctor–patient communication (DPC), understand why doctors and patients use repetition, and improve the current deficiencies in online medical platforms. MethodsThe study performed a content analysis of 72 sets of textual doctor–patient conversations on the Chinese online medical consultation platform Chunyu Doctor. ResultsRepetitions occurred 1412 times in the 72 sets of online DPC. Patient self-repetitions were the most prevalent (30.7 %), while patient repetitions of doctors were the least common (17.1 %). Doctors used repetitions for explanations and affirmations. Patients used repetition for emphasis, verification, and turn-taking. The repetition frequencies of doctors and patients were primarily influenced by personal factors. However, doctor-dimension factors exerted a greater impact on the frequency of patient repetitions. ConclusionsThe reasons for repetitions in online DPC differ from those offline. Online DPC increases patient initiative and reduces doctor authority. Nevertheless, it could be affected by gender stereotypes generated during offline consultations. Doctors still dominated the conversations but attended sufficiently to the patient discourse. Online DPC is gradually attempting to fulfill the expectations of a new patient-centered healthcare pattern. Practical implicationsThe findings yield suggestions for healthcare providers and the designers of online healthcare platforms.

A physician-nurse partnership &lt;i&gt;via&lt;/i&gt; online healthcare platforms protects infertile women from anxiety and depression: A multi-center prospective study from Shanghai, China

Effective health interventions are a priority for future infertility research, and effective interventions in patient-centered care are still needed. A multi-center prospective study was conducted in order to investigate the effects of a physician-nurse partnership (patients receive guidance and health education via online healthcare platforms) on depression and anxiety disorders in infertile women. The women were randomly assigned to a physician-nurse partnership group (n = 90) or a routine treatment group (n = 90). The primary endpoints were self-rating anxiety scale and self-rating depression scale scores. This study also examined the waiting time as an outpatient and the frequency of using online medical platforms. Compared to the routine treatment group, scores on the self-rating anxiety scale (48.4) and the self-rating depression scale (48.0) were significantly lower in the physician-nurse partnership group (p = 0.004, p = 0.001). Moreover, the mean waiting time (3.4) was shorter and online platforms (6.1) were used more frequently in the physician-nurse partnership group than in the routine treatment group (p < 0.001, p < 0.001). These data suggest that a physician-nurse partnership could reduce patients' anxiety, depression, and their waiting time as an outpatient.

A Comparison Study of Doctor-Patient Internet Interactions in Traditional and Modern Medicine: Empirical Evidence from Online Healthcare Communities

Online healthcare platforms serve not just as a medical knowledge-sharing community but also bring about effective interactions between professional physicians and patients. However, it is unclear whether online technology adoption affects such interactions in the same way between traditional Chinese medicine and modern medical departments. By utilizing a large sample of online doctor-patient interaction information from 168,870 doctor-specific interactive webpages recorded in a famous Chinese online healthcare community, this paper studies the differences between 17,513 traditional medicine doctor homepages and 151,357 others from more than 100 different specialty areas. Our chosen platform is representative since it covers about 800,000 physicians working at over 10,000 hospitals across all major provincial regions in China. We document that online medical service users tend to accept and use online health care services. However, patients seeing Chinese medicine doctors exhibit the following unique characteristics. They still prefer choosing doctors according to third-party information and may be reluctant to pay for the current online service price level. This problem is hard to overcome by the platform in the short run. Patients need a long-term process to adapt to the upgraded medical environment gradually. Therefore, establishing a personalized doctor recommendation system has become the most urgent demand presently.

Can Patients Co-Create Value in an Online Healthcare Platform? An Examination of Value Co-Creation.

With the development of online healthcare services, patients could receive support and create value with other users on online healthcare platforms. However, little research has been conducted on the internal mechanisms of patient value co-creation from the perspective of online healthcare platforms. To analyze patient value co-creation in online healthcare platforms, this study explores the underlying mechanisms of patient value co-creation among patients. The results show that value co-creation includes patient citizenship behavior and participation behavior. Information quality, peer communication, and system quality influence functional experiences and emotional experiences. In addition, functional experiences and emotional experiences could influence patient value co-creation. This study clarifies the mechanism of value co-creation among patients and provides insight into value co-creation in online healthcare platforms.