Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study.
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ABSTRACT:
SUBMITTER: Ramdani B
PROVIDER: S-EPMC7388041 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
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Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study.
Ramdani Boumediene B Duan Binheng B Berrou Ilhem I
JMIR medical informatics 20200714 7
<h4>Background</h4>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.<h4>Objective</h4>This study aims to explore the determinants of mHealth adoption by hospitals using the technology-organization-environment (TOE) framework.<h4>Methods</h4>We conducted an interviewer-administered survey with 87 managers in Chinese public hospita ...[more]
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Project description:IntroductionSub-Saharan Africa lags in adoption of mobile health (m-health) applications and in leveraging m-health for sustainable development goals. There is a need for a comprehensive investigation of determinants of hospitals' adoption of m-health in Sub-Saharan Africa to inform policies, practices and investments.MethodsThis investigation used a logit regression model to analyze the determinants of m-health adoption in Kenyan hospitals applying the Technological, Organizational and Environmental (TOE) framework and the Diffusion of Innovation (DOI) theory. A representative sample of 211 executives of Level 4-6 hospitals in 24 counties provided primary data on Patient-Centered (PC) and Facility-Centered (FC) m-health applications.ResultsBoth PC and FC m-health adoption were predicted by competition for patients (PC p = 0.041, FC p = 0.021), IT human resource capacity (PC p = 0.048, FC p = 0.037), and hospital pursuit of market growth through technological leadership (PC p = 0.010, FC p = 0.020). Further determinants of PC m-health adoption included hospital access to slack financial resources (p = 0.006), acquisition strategy (p = 0.011), compatibility with the hospital systems (p = 0.015), trialability (p = 0.019), medical insurance company support (p = 0.025),patient pressure (p = 0.036), and perceived effect of global medical tourism (p = 0.039). FC m-health adoption was predicted by hospital size (p = 0.008), ICT infrastructure capacity (p = 0.041), and government support (p = 0.013).ConclusionA differentiated approach is required to scale up m-health adoption. PC m-health requires emphasis on establishing national and regional compatibility and interoperability, developing trialability processes and validation mechanisms, incentivizing patient competition and mobility, establishing innovative and cost-effective acquisition strategies, and ensuring integration of digital services within national insurance schemes and policies. These policies require support from patients and communities to drive demand and spur investment in adequate IT human resources to maintain reliability. Pilot PC m-health projects should prioritize hospitals with slack financial resources, while FC m-health should target large facility size. FC m-health applications are more complex and costly than PC, requiring government incentives to trigger hospital investments and national investment in ICT infrastructure. Investors and hospital managers should integrate m-health into market growth strategies for sustainable m-health scale-up in Kenya and beyond.
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Project description:The aim of this study is to investigate the determinants for the intention to adopt mobile technology as a data collection methodology in market research projects. A conceptual framework was developed using the technology-organization-environment (TOE) model to identify technological factors (perceived benefits and limitations), organizational factors (open attitude toward change, professional competence, satisfaction with traditional systems, and firm size), and environmental factors (industry pressure, client pressure, and participant pressure) affecting adoption. The empirical study was performed with data from 67 firms in the Spanish market research industry, which were analyzed using partial least squares (PLS). The results suggest that only organizational and environmental factors have a significant influence on adoption. Key factors include professional competence, organizational openness, satisfaction with traditional and online methodologies, and pressure from industry, clients and survey participants. The findings reveal that technological characteristics are no longer a driver, as firms are starting to adopt mobile marketing research based on its greater convenience for participants, and as an element of strategic differentiation.
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Project description:BACKGROUND:The potential benefit of mobile health (M-Health) in developing countries for improving the efficiency of health care service delivery, health care quality, and patient safety, as well as reducing cost, has been increasingly recognized and emphasized in the last few years. OBJECTIVE:Limited research has investigated the facilitators and barriers for the adoption of M-Health in developing countries to secure successful implementation of the technology. To fill this knowledge gap, we propose an integrative model that explains the patient's adoption behavior of M-Health in developing countries grounded on the unified theory of acceptance and use of technology, dual-factor model, and health belief model. METHOD:We empirically tested and evaluated the model based on data collected using a survey method from 280 patients living in a developing country. Partial least squares (PLS-SEM) technique was used for data analysis. RESULTS:The results showed that performance expectancy, effort expectancy, social influence, perceived health threat, M-Health app quality, and life quality expectancy have a direct positive effect on patients' intention to use M-Health. The results also showed that security and privacy risks have a direct negative effect on the patient's intention to use M-Health. However, resistance to change was found to have an indirect negative effect on patients' intention to use M-Health through the performance expectancy. CONCLUSION:The research contributes to the existing literature of health information systems and M-Health by better understanding how technological, social, and functional factors are associated with digital health applications and services use and success in the context of developing countries. With the widespread availability of mobile technologies and services and the growing demand for M-Health apps, this research can help guide the development of the next generation of M-Health apps with a focus on the needs of patients in developing countries. The research has several theoretical and practical implications for the health care industry, government, policy makers, and technology developers and designers.
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Project description:BackgroundOnline hospitals are part of an innovative model that allows China to explore telemedicine services based on national conditions with large populations, uneven distribution of medical resources, and lack of quality medical resources, especially among residents needing to be protected from COVID-19 infection.ObjectiveIn this study, we built a hypothesis model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) in order to analyze the factors that may influence patients’ willingness to use mobile medical services. This research was designed to assist in the development of mobile medical services. Residents who do not live in urban areas and cannot access medical assistance would greatly benefit from this research, as they could immediately go to the online hospital when needed.MethodsA cross-sectional study based at the West China Hospital, Sichuan University, was conducted in July 2020. A total of 407 respondents, 18 to 59 years old, in Western China were recruited by convenience sampling. We also conducted an empirical test for the hypothesis model and applied structural equation modeling to estimate the significance of path coefficients so that we could better understand the influencing factors.ResultsOut of 407 respondents, 95 (23.3%) were aware of online hospitals, while 312 (76.7%) indicated that they have never heard of online hospitals before. Gender (P=.048) and education level (P=.04) affected people's willingness to use online hospitals, and both of these factors promoted the use of online hospitals (odds ratio [OR] 2.844, 95% CI 1.010-8.003, and OR 2.187, 95% CI 1.031-4.636, respectively). According to structural equation modeling, the results of the path coefficient analysis indicated that performance expectancy, effort expectancy, and facilitating conditions have positive effects on patients' willingness to use online hospitals.ConclusionsThe goal of our research was to determine the factors that influence patients' awareness and willingness to use online hospitals. Currently, the public's awareness and usage of online hospitals is low. In fact, effort expectancy was the most important factor that influenced the use of online hospitals; being female and having a high education also played positive roles toward the use of mobile medical services.
| S-EPMC7869921 | biostudies-literature
Project description:With the increasing concerns about the health of individuals in China and the development of information technology, mHealth enables patients to access health information and interact with doctors anytime and anywhere. Examining patients' willingness to use mHealth is considered critical because its success depends on the adoption of patients.The objective of our study was to explore the determinants of mHealth service adoption among Chinese patients using an extended technology acceptance model (TAM) with trust and perceived risks.We conducted a questionnaire-based survey in 3 large hospitals in China and analyzed the data using structural equation modeling.The results corroborated that the proposed model fits well. Trust, perceived usefulness, and perceived ease of use positively correlated with mHealth service adoption. Privacy and performance risks negatively correlated with the patients' trust and adoption intention toward mHealth services. In addition, patients' age and chronic diseases can help predict their trust level and adoption intention toward mHealth, respectively.We concluded that the TAM generally works in the context of mHealth adoption, although its significance has declined. In addition to technical factors, trust and perceived risks are critical for explaining mHealth service adoption among Chinese patients.
| S-EPMC6135967 | biostudies-literature
Project description:BackgroundMobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians' participation in mHealth apps.ObjectiveThis study aims to empirically explore factors affecting physicians' usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings.MethodsWe collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians' usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias.ResultsOur results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians' cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention.ConclusionsThis study contributes to the existing literature on UTAUT2 extension of physicians' acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians' usage behaviors on mHealth apps in China and provide such apps' managers with an insight into the promotion of physicians' active acceptance and usage behaviors.
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Project description:BackgroundDietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey's most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users.ObjectiveThe aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users.MethodsWe conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling.ResultsStatistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app.ConclusionsThis research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform's dietitians to ensure that users' interactions with the dietitians are based on trust for the platform and the brand.
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Project description:ObjectiveSmartphone apps and mobile devices are an emerging method of healthcare data collection. This study sought to understand how physicians currently view mobile health (mHealth) technologies and use them in patient care.MethodsA total of 186 physicians affiliated with Washington University School of Medicine in St. Louis, Missouri, USA completed a survey in 2016 regarding their current implementation of mHealth technologies for patient care and support for further development.ResultsMore than half of respondents were willing to discuss health apps and mobile devices with patients. However, most were not currently recommending them to patients. Apps/devices that encouraged a healthy diet and weight or tracked heart rate received the highest satisfaction ratings. Apps/devices that accessed the EMR (electronic medical record) remotely, provided medication reminders, or enrolled research subjects garnered the most interest despite respondents lacking prior experience. A majority agreed that collected biometrics are useful for promoting a healthy lifestyle (68%), tracking medical treatment (64%), or conducting research (56%); and agreed that proof of accuracy and precision (81%) and the efficient integration of collected data (68%) are necessary improvements. Uploading data automatically and updating physicians in real-time was the most preferred method of data integration into the EMR.ConclusionsPhysicians show interest in using mHealth technologies for patient care but have limited experience, usually with those specific to their specialties. Proof of quality and a method to integrate data into the EMR are necessary for a mainstream role in healthcare.
| S-EPMC7036486 | biostudies-literature
Project description:BackgroundOnline health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients' diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients' intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients' acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China.MethodsBased on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients' information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing.ResultsPrice value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention.ConclusionsBased on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.
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Project description:INTRODUCTION:Electronic Health Records are receiving considerable attention as a valuable tool for managing clinical information. Despite the prospects of Electronic Health Records in developing countries, many pre-implementation assessments target organizational, managerial, and infrastructural readiness, but barely include a detailed examination of health provider readiness. Meanwhile, health provider readiness is a critical success factor for electronic health records in settings where the majority of the workforce is less likely to have basic computer skills. We sought to assess the readiness of health providers for electronic health records in Ghana. MATERIALS AND METHOD:An institutional-based cross-sectional study was conducted among 350 health providers in northern Ghana from June-September 2019. Data were collected using a modified questionnaire on provider readiness. The mean overall readiness was calculated for each respondent. Providers with readiness score below the overall mean score were categorized as not being ready while those at or above the mean score were considered ready. Multiple linear regression was conducted to determine the factors that determine provider readiness. RESULTS:Two hundred and nine health providers responded to the questionnaire (59.7 response rate). The mean overall readiness was 3.61 (SD = .76), mean core readiness was 3.74 (SD = .80), and mean engagement readiness was 3.47 (SD = .67). Using the average overall readiness score as the cut-off for determining being ready and not ready for electronic health records, overall readiness was 54.9%, core readiness was 67.2%, while engagement readiness was 43.1%. Age, sex, old employees compared to new employees, computer literacy, and knowledge of electronic health records were significant determinants of health providers' readiness to adopt electronic health records. CONCLUSION:We observed that health providers were marginally ready for electronic health records adoption. While participants might have expressed dissatisfaction with paper-based records and expressed a desire for electronic health records, they expressed fear of the potential impact of computerized records. We proposed a robust informatics curriculum and capacity building workshops for improving provider readiness for electronic health records.
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