How Resource Scarcity and Accessibility Affect Patients' Usage of Mobile Health in China: Resource Competition Perspective

Advancing mHealth Research in Low-Resource Settings: Young Women's Insights and Implementation Challenges with Wearable Smartwatch Devices in Uganda

In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants' experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala's slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues.

Equity of health resource allocation in Chongqing, China, in 2021: a cross-sectional study

BACKGROUND

Chongqing, the most populous city in Southwest China. This study aims to examine the equity of health resource allocation in Chongqing using the latest statistics, analyse possible shortcomings and propose strategies to address these issues.

METHODS

This cross-sectional study used healthcare resource, population, area and gross domestic product data from the Seventh National Census Bulletin of Chongqing, the National County Statistical Yearbook, the Chongqing Municipal Bureau of Statistics and the Chongqing Health Statistical Yearbook 2022. We also studied the equity of health resource allocation in Chongqing by using the Gini coefficient, Lorenz curve and Theil index, and used the Analytical Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) method to comprehensively evaluate the health resources in the four major regions of Chongqing.

RESULTS

The Gini coefficient of health resources in Chongqing in 2021 was the highest when allocated according to geographical area, between 0.4285 and 0.6081, both of which exceeded 0.4, and the Gini coefficient of medical equipment was the highest and exceeded 0.6. The inter-regional Theil index of each resource was greater than the intraregional Theil index, and the contribution of inter-regional differences ranged from 64.83% to 80.21%. The results of the AHP-TOPSIS method showed that the relative proximity between health resources and ideal solutions in four regions of Chongqing ranged from 0.0753 to 0.9277.

CONCLUSION

The allocation of health resources in Chongqing exhibits pronounced inequities, particularly in the distribution of medical equipment according to geographical area. Moreover, there exists a substantial gap in the equity of health resource allocation among the four regions of Chongqing. As such, this study emphasises the need for Chongqing, China, to prioritise the equitable allocation of health resources and increase consideration of geographic factors. Implementing measures to promote equitable allocation of health resources, particularly in geographic terms, is critical.

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Based on the online patient-physician communication data, this study used natural language processing and machine learning algorithm to construct a medical intelligent guidance and recommendation model. First, based on 16,935 patient main complaint data of nine diseases, this study used the word2vec, long-term and short-term memory neural networks, and other machine learning algorithms to construct intelligent department guidance and recommendation model. Besides, taking ophthalmology as an example, it also used the word2vec, TF-IDF, and cosine similarity algorithm to construct an intelligent physician recommendation model. Furthermore, to recommend physicians with better service quality, this study introduced the information amount of physicians' feedback to the recommendation evaluation indicator as the text and voice service quality. The results show that the department guidance model constructed by long-term and short-term memory neural networks has the best effect. The precision is 82.84%, and the F1-score is 82.61% in the test set. The prediction effect of the LSTM model is better than TextCNN, random forest, K-nearest neighbor, and support vector machine algorithms. In the intelligent physician recommendation model, under certain parameter settings, the recommendation effect of the hybrid recommendation model based on similar patients and similar physicians has certain advantages over the model of similar patients and similar physicians.

Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model

Background

With the rapid development of "Internet + medicine" and the impact of the COVID-19 epidemic, online health communities have become an important way for patients to seek medical treatment. However, the mistrust between physicians and patients in online health communities has long existed and continues to impact the decision-making behavior of patients. The purpose of this article is to explore the influencing factors of patient decision-making in online health communities by identifying the relationship between physicians' online information and patients' selection behavior.

Methods

In this study, we selected China's Good Doctor (www.haodf.com) as the source of data, scrapped 10,446 physician data from December 2020 to June 2021 to construct a logit model of online patients' selection behavior, and used regression analysis to test the hypotheses.

Results

The number of types of services, number of scientific articles, and avatar in physicians' personal information all has a positive effect on patients' selection behavior, while the title and personal introduction hurt patients' selection behavior. Online word-of-mouth positively affected patients' selection behavior and disease risk had a moderating effect.

Conclusion

Focusing on physician-presented information, this article organically combines the Elaboration likelihood model with trust source theory and online word-of-mouth from the perspective of the trusted party-physician, providing new ideas for the study of factors influencing patients' selection behavior in online health communities. The findings provide useful insights for patients, physicians, and community managers about the relationship between physician information and patients' selection behavior.

Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT

With the deepening of population aging in China, chronic diseases are a major public health concern that threatens the life and health of nationals. Mobile health or mHealth can effectively monitor chronic diseases, which holds vital significance to the alleviation of social pressure caused by aging. To patients with chronic diseases, mHealth cannot give full play to its value, only when it is used in the long term. However, there is not yet research exploring mHealth continuance intention from the perspective of elders with chronic diseases. So, this research represents the first attempt to empirically analyze mHealth continuance intention from the perspective of elders with chronic diseases. The purpose of this research is to make up the research gap of the mHealth field and to put forward theoretical and practical implications based on research results. To obtain research data, a questionnaire was conducted. A total of 926 copies were collected online and 527 copies were collected offline. The structural equation model (SEM) was used for data analysis. Research results suggest that confirmation can significantly influence satisfaction, performance expectancy and effort expectancy. Meanwhile, confirmation and performance expectancy can significantly influence satisfaction. Additionally, effort expectancy, performance expectancy, social influence and facilitating conditions can directly and significantly influence continuance intention. Among them, performance expectancy can directly influence continuance intention in the most significant way. This research provides solid evidence for the validity of the integrated model of ECM-ISC and UTAUT in the mHealth field, which can be a theoretical basis for mHealth operators' product R&D.

Patient's decision and experience in the multi-channel appointment context: An empirical study

Background

Long waiting time for treatment in the outpatient department has long been a complaint and has influenced patient's experience. It is critical to schedule patients for doctors to reduce patient's waiting time. Nowadays, multi-channel appointment has been provided for patients to get medical services, especially for those with severe illnesses and remote distance. This study aims to explore the factors that influence patient appointment channel choice in the context of multi-channel appointments, and how channel choice affects the waiting time for offline visiting.

Methods

We collected outpatient appointment records from both online and offline appointment channels to conduct our empirical research. The empirical analysis is conducted in two steps. We first analyze the relationship between appointment channel choice and patient's waiting time and then the relationships between three determinants and appointment channel choice. The ordinary least squares and the logistic regression model are used to obtain the empirical results.

Results

Our results show that a patient with an online appointment decision has a shorter consultation waiting time compared with a patient with on-site appointment (β = −0.320, p < 0.001). High-quality resource demand (β = 0.349, p < 0.001), high-severity disease (β = 0.011, p < 0.001), and high non-disease costs (β = 0.039, p < 0.001) create an obvious incentive for patients to make appointments via the Internet. Further, only the effect of non-disease cost on channel choice is lower for patients with multiple visit histories (β = −0.021, p < 0.001).

Conclusions

Our study confirms the effect of Internet use on reducing patient's waiting time. Patients consider both health-related risk factors and cost-related risk factors to make decisions on appointment channels. Our study produces several insights, which have implications for channel choice and patient's behavior literature. More importantly, these insights contribute to the design of appointment systems in hospitals.

Factors Affecting Customer Intention to Adopt a Mobile Chronic Disease Management Service

The Mobile Chronic Disease Management Service (MCDMS) is an emerging medical service for chronic disease prevention and treatment, but limited attention has been paid to the factors that affect users’ intention to adopt the service. Based on the unified theory of acceptance and use of technology 2 and the protection motivation theory, the authors built an MCDMS adoption model. The authors also verified the differentiating age effect on the service adoption intention from experiential distance perspective of the construal level theory. Empirical results showed that the young group focused more on the impact of effort expectancy, whereas the elderly group focused more on performance expectancy, imitating others, and perceived severity. Furthermore, the young group, however, focused more on the impact of perceived vulnerability, and offline medical habits showed no significant influence on either group’s intention to adopt, which were not consistent with the original hypotheses. The findings can aid MCDMS providers in selecting marketing strategies targeted toward different age groups.

Determining Factors Affecting the Users' Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support

As the national awareness of health keeps deepening, online health communities (OHCs) have achieved rapid development. Users' participation is critically important to the sustainable development of OHCs. Nevertheless, users usually lack the motive for participation. Based on the social capital theory, this research examines factors influencing users' participation in OHCs. The purpose of this research is to find out decisive factors that influence users' participation in OHCs, enrich the understanding of users' participation in OHCs, and help OHCs address the issue of sustainable development. The research model was empirically tested using 1277 responses from an online survey conducted in China. Data was analyzed using the structural equation modeling (SEM). We found informational support and emotional support to have significant direct effects over the structural capital, relational capital and cognitive capital of OHCs. Meanwhile, it is observed that relational capital and cognitive capital degree have a significant influence on knowledge acquisition and knowledge contribution of OHCs. For researchers this study provides a basis for further refinement of individual models of users' participation. For practitioners, understanding the social capital is crucial to users' knowledge acquisition and knowledge contribution that achieve high participation in OHCs.

The Association between mHealth App Use and Healthcare Satisfaction among Clients at Outpatient Clinics: A Cross-Sectional Study in Inner Mongolia, China

Mobile health (mHealth) applications (apps) have been developed in hospital settings to allocate and manage medical care services, which is one of the national strategies to improve health care in China. Little is known about the comprehensive effects of hospital-based mHealth app use on client satisfaction. The aim of this study was to determine the relationship between the full range of mHealth app use and satisfaction domains among clients attending outpatient clinics. A cross-sectional survey was conducted from January to February 2021 in twelve tertiary hospitals in Inner Mongolia. After the construction of the mHealth app use, structural equation modeling was used for data analysis. Of 1889 participants, the standardized coefficients β on environment/convenience, health information, and medical service fees were 0.11 (p < 0.001), 0.06 (p = 0.039), and 0.08 (p = 0.004), respectively. However, app use was not significantly associated with satisfaction of doctor−patient communication (β = 0.05, p = 0.069), short-term outcomes (β = 0.05, p = 0.054), and general satisfaction (β = 0.02, p = 0.429). Clients of the study hospitals were satisfied with the services, but their satisfaction was not much associated with mHealth use. The limitation of the mHealth system should be improved to enhance communication and engagement among clients, doctors, and healthcare givers, as well as to pay more attention to health outcomes and satisfaction of clients.

Sociotechnical Factors Affecting Patients' Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis

BACKGROUND

Mobile health (mHealth) tools have emerged as a promising health care technology that may contribute to cost savings, better access to care, and enhanced clinical outcomes; however, it is important to ensure their acceptance and adoption to harness this potential. Patient adoption has been recognized as a key challenge that requires further exploration.

OBJECTIVE

The aim of this review was to systematically investigate the literature to understand the factors affecting patients' adoption of mHealth tools by considering sociotechnical factors (from technical, social, and health perspectives).

METHODS

A structured search was completed following the participants, intervention, comparators, and outcomes framework. We searched the MEDLINE, PubMed, Cochrane Library, and SAGE databases for studies published between January 2011 and July 2021 in the English language, yielding 5873 results, of which 147 studies met the inclusion criteria. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Handbook were followed to ensure a systematic process. Extracted data were analyzed using NVivo (QSR International), with thematic analysis and narrative synthesis of emergent themes.

RESULTS

The technical factors affecting patients' adoption of mHealth tools were categorized into six key themes, which in turn were divided into 20 subthemes: usefulness, ease of use, data-related, monetary factors, technical issues, and user experience. Health-related factors were categorized into six key themes: the disease or health condition, the care team's role, health consciousness and literacy, health behavior, relation to other therapies, integration into patient journey, and the patients' insurance status. Social and personal factors were divided into three key clusters: demographic factors, personal characteristics, and social and cultural aspects; these were divided into 19 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them.

CONCLUSIONS

This review builds on the growing body of research that investigates patients' adoption of mHealth services and highlights the complexity of the factors affecting adoption, including personal, social, technical, organizational, and health care aspects. We recommend a more patient-centered approach by ensuring the tools' fit into the overall patient journey and treatment plan, emphasizing inclusive design, and warranting comprehensive patient education and support. Moreover, empowering and mobilizing clinicians and care teams, addressing ethical data management issues, and focusing on health care policies may facilitate adoption.

Socio-demographic digital divide in mHealth app use among clients at outpatient departments in Inner Mongolia, China: a cross-sectional study (Preprint)

Background

Mobile health (mHealth) apps have become part of the infrastructure for access to health care in hospitals, especially during the COVID-19 pandemic. However, little is known about the effects of sociodemographic characteristics on the digital divide regarding the use of hospital-based mHealth apps and their benefits to patients and caregivers.

Objective

The aim of this study was to document the cascade of potential influences from digital access to digital use and then to mHealth use, as well as the potential influence of sociodemographic variables on elements of the cascade.

Methods

A cross-sectional survey was conducted from January to February 2021 among adult clients at outpatient departments in 12 tertiary hospitals of Inner Mongolia, China. Structural equation modeling was conducted after the construct comprising digital access, digital use, and mHealth use was validated.

Results

Of 2115 participants, the β coefficients (95% CI) of potential influence of digital access on digital use, and potential influence of digital use on mHealth use, were 0.28 (95% CI 0.22-0.34) and 0.51 (95% CI 0.38-0.64), respectively. Older adults were disadvantaged with regard to mHealth access and use (β=–0.38 and β=–0.41), as were less educated subgroups (β=–0.24 and β=–0.27), and these two factors had nonsignificant direct effects on mHealth use.

Conclusions

To overcome the mHealth use divide, it is important to improve digital access and digital use among older adults and less educated groups.

Attitudes and perspectives of 534 Chinese pediatricians toward internet hospitals

BACKGROUND

Internet hospitals introduced in China have effectively reduced service time and space, promoted high-quality pediatric medical resources to grassroots areas, solved the contradiction between supply and demand of pediatric medical resources, and met patients' increasing multi-level and diversified medical service needs. However, pediatricians' attitudes toward and satisfaction with the use of internet hospitals remain unknown.

OBJECTIVE

This study aimed to investigate pediatricians' knowledge of, use of, and satisfaction with internet hospitals in order to identify major issues in internet hospital development, and to understand pediatricians' attitudes and opinions on the construction, development, and use of internet hospitals.

MATERIALS AND METHODS

A total of 625 pediatricians in 17 public tertiary hospitals in Shanghai were surveyed from November 1-30, 2021. Five hundred and thirty four pediatricians completed the survey, and the response rate was 85.44%. Pediatricians' baseline demographic data were collected and information about their use of and satisfaction with internet hospitals.

RESULTS

About 70.22% (375/534) of pediatricians knew about internet hospitals and about 54.68% (292/534) use internet hospitals for patient consultation, diagnosis, and treatment. Utilized services mainly focused on online consultation (271/292, 92.81%), online follow-up consultation (174/292, 59.59%), and health sciences (111/292, 38.01%). Online services were provided by 69.18% (202/292) of pediatricians for less than 1 h a day, and 75.00% (219/292) responded to fewer than five patient consultations online every day. Pediatricians' overall satisfaction with internet hospitals was low (3.59 ± 0.92 points), user experience, systems functions, operation processes, service prices, and performance rewards of internet hospitals were main influencing factors. Pediatricians are enthusiastic about further development of internet hospitals, with 87.83% (469/534) willing to provide services on the internet hospital platform.

CONCLUSION

Most pediatricians view internet hospitals favorably and are eager to contribute to the development of online diagnosis and treatment services. The development of internet hospitals will be more strongly supported by improving pediatricians' satisfaction and mobilizing their enthusiasm and initiative to participate in internet medical services.

The Digital Divide in Technologies for Autism: Feasibility Considerations for Low- and Middle-Income Countries

Digital technologies have the potential to empower individuals with autism and their families. The COVID-19 pandemic emphasized and accelerated the drive towards technology for information, communication, training, clinical care and research, also in the autism community. However, 95% of individuals with autism live in low- and middle-income countries (LMIC) where access to electricity, internet and the ever-increasing range of digital devices may be highly limited. The World Bank coined the term ‘the digital divide’ to describe the disparities in access to digital technologies between high-income and LMIC contexts. Here we evaluated the feasibility of six emerging technologies for autism spectrum disorders, and reflected on key considerations for implementation in LMIC contexts to ensure that we do not inadvertently widen the pre-existing digital divide.

Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China

BACKGROUND

The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China's health economy, and then find evidence that affects patient mobility.

METHODS

Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books. The gravitational value of China's health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis.

RESULTS

A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects.

CONCLUSION

There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces' positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.

Determinants of Patients' Intention to Use the Online Inquiry Services Provided by Internet Hospitals: Empirical Evidence From China

Background

Internet hospitals show great potential for adequately fulfilling people’s demands for high-quality outpatient services, and with the normalization of the epidemic prevention and control of COVID-19, internet hospitals play an increasingly important role in delivering health services to the public. However, the factors that influence patients’ intention to use the online inquiry services provided by internet hospitals remain unclear. Understanding the patients’ behavioral intention is necessary to support the development of internet hospitals in China and promote patients’ intention to use online inquiry services provided by internet hospitals during the prevention and control of the COVID-19 epidemic.

Objective

The purpose of this study is to identify the determinants of patients’ intention to use the online inquiry services provided by internet hospitals based on the theory of planned behavior (TPB).

Methods

The hypotheses of our research model were developed based on the TPB. A questionnaire was developed through patient interviews, verified using a presurvey, and used for data collection for this study. The cluster sampling technique was used to include respondents with chronic diseases. Structural equation modeling was used to test the research hypotheses.

Results

A total of 638 valid responses were received from patients with chronic diseases. The goodness-of-fit indexes corroborated that the research model was a good fit for the collected data. The model explained 45.9% of the variance in attitude toward the behavior and 60.5% of the variance in behavioral intention. Perceived behavioral control and perceived severity of disease had the strongest total effects on behavioral intention (β=.624, P=.004 and β=.544, P=.003, respectively). Moreover, perceived convenience, perceived information risk, emotional preference, and health consciousness had indirect effects on behavioral intention, and these effects were mediated by attitude toward the behavior. Among the four constructs, perceived convenience had the highest indirect effect on behavioral intention (β=.207; P=.001).

Conclusions

Perceived behavioral control and perceived severity of disease are the most important determinants of patients’ intention to use the online inquiry services provided by internet hospitals. Therefore, internet hospitals should further optimize the design of online service delivery and ensure a reasonable assembly of high-quality experts, which will benefit the promotion of patients’ adoption intention toward online inquiry services for health purposes. Perceived convenience, emotional preference, and perceived risks also have effects on behavioral intention. Therefore, the relevant quality control standards and regulations for internet hospitals should be further developed and improved, and the measures to protect personal information should be strengthened to ensure the patient safety. Our study supports the use of the TPB in explaining patients’ intention to use online inquiry services provided by internet hospitals.

The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis

Background

The internet hospital is an innovative organizational form and service mode under the tide of internet plus in the Chinese medical industry. It is the product of the interaction between consumer health needs and supply-side reform. However, there has still been no systematic summary of its establishment and definition, nor has there been an analysis of its service content.

Objective

The primary purpose of this study was to understand the definition, establishment, and development status of internet hospitals.

Methods

Data on internet hospitals were obtained via the Baidu search engine for results up until January 1, 2019. Based on the results of the search, we obtained more detailed information from the official websites and apps of 130 online hospitals and formed a database for descriptive analysis.

Results

By January 2019, the number of registered internet hospitals had expanded to approximately 130 in 25 provinces, accounting for 73.5% of all provinces or province-level municipalities in China. Internet hospitals, as a new telehealth model, are distinct but overlap with online health, telemedicine, and mobile medical. They offer four kinds of services—convenience services, online medical services, telemedicine, and related industries. In general, there is an underlying common treatment flowchart of care in ordinary and internet hospitals. There are three different sponsors—government-led integration, hospital-led, and enterprise-led internet hospitals—for which stakeholders have different supporting content and responsibilities.

Conclusions

Internet hospitals are booming in China, and it is the joint effort of the government and the market to alleviate the coexistence of shortages of medical resources and wasted medical supplies. The origin of internet hospitals in the eastern and western regions, the purpose of the establishment initiator, and the content of online and offline services are different. Only further standardized management and reasonable industry freedom can realize the original intention of the internet hospital of meeting various health needs.

Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study

BACKGROUND

Although mobile health (mHealth) has the potential to transform health care by delivering better outcomes at a much lower cost than traditional health care services, little is known about mHealth adoption by hospitals.

OBJECTIVE

This study aims to explore the determinants of mHealth adoption by hospitals using the technology-organization-environment (TOE) framework.

METHODS

We conducted an interviewer-administered survey with 87 managers in Chinese public hospitals and analyzed the data using logistic regression.

RESULTS

The results of our survey indicate that perceived ease of use (β=.692; P<.002), system security (β=.473; P<.05), top management support (β=1.466; P<.002), hospital size (β=1.069; P<.004), and external pressure (β=.703; P<.005) are significantly related to hospitals' adoption of mHealth. However, information technology infrastructure (β=.574; P<.02), system reliability (β=-1.291; P<.01), and government policy (β=2.010; P<.04) are significant but negatively related to hospitals' adoption of mHealth.

CONCLUSIONS

We found that TOE model works in the context of mHealth adoption by hospitals. In addition to technological predictors, organizational and environmental predictors are critical for explaining mHealth adoption by Chinese hospitals.

How Online Reviews and Services Affect Physician Outpatient Visits: Content Analysis of Evidence From Two Online Health Care Communities

Background

Online healthcare communities are changing the ways of physician-patient communication and how patients choose outpatient care physicians. Although a majority of empirical work has examined the role of online reviews in consumer decisions, less research has been done in health care, and endogeneity of online reviews has not been fully considered. Moreover, the important factor of physician online services has been neglected in patient decisions.

Objective

In this paper, we addressed the endogeneity of online reviews and examined the impact of online reviews and services on outpatient visits based on theories of reviews and channel effects.

Methods

We used a difference-in-difference approach to account for physician- and website-specific effects by collecting information from 474 physician homepages on two online health care communities.

Results

We found that the number of reviews was more effective in influencing patient decisions compared with the overall review rating. An improvement in reviews leads to a relative increase in physician outpatient visits on that website. There are channel effects in health care: online services complement offline services (outpatient care appointments). Results further indicate that online services moderate the relationship between online reviews and physician outpatient visits.

Conclusions

This study investigated the effect of reviews and channel effects in health care by conducting a difference-in-difference analysis on two online health care communities. Our findings provide basic research on online health care communities.