Role of Incentives in the Use of Blockchain-Based Platforms for Sharing Sensitive Health Data: Experimental Study.

BACKGROUND: Blockchain is an emerging technology that enables secure and decentralized approaches to reduce technical risks and governance challenges associated with sharing data. Although blockchain-based solutions have been suggested for sharing health information, it is still unclear whether a suitable incentive mechanism (intrinsic or extrinsic) can be identified to encourage individuals to share their sensitive data for research purposes. OBJECTIVE: This study aimed to investigate how important extrinsic incentives are and what type of incentive is the best option in blockchain-based platforms designed for sharing sensitive health information. METHODS: In this study, we conducted 3 experiments with 493 individuals to investigate the role of extrinsic incentives (ie, cryptocurrency, money, and recognition) in data sharing with research organizations. RESULTS: The findings highlight that offering different incentives is insufficient to encourage individuals to use blockchain technology or to change their perceptions about the technology's premise for sharing sensitive health data. The results demonstrate that individuals still attribute serious risks to blockchain-based platforms. Privacy and security concerns, trust issues, lack of knowledge about the technology, lack of public acceptance, and lack of regulations are reported as top risks. In terms of attracting people to use blockchain-based platforms for data sharing in health care, we show that the effects of extrinsic motivations (cryptoincentives, money, and status) are significantly overshadowed by inhibitors to technology use. CONCLUSIONS: We suggest that before emphasizing the use of various types of extrinsic incentives, the users must be educated about the capabilities and benefits offered by this technology. Thus, an essential first step for shifting from an institution-based data exchange to a patient-centric data exchange (using blockchain) is addressing technology inhibitors to promote patient-driven data access control. This study shows that extrinsic incentives alone are inadequate to change users' perceptions, increase their trust, or encourage them to use technology for sharing health data.

How learner engagement impacts non-formal online learning outcomes through value co-creation: an empirical analysis.

From the perspective of service science and its core concept of value co-creation, promoting learner engagement is critical for learning outcomes in a non-formal online learning environment. To promote online learning performance, we study how multidimensional learner engagement affects both instrumental and experiential learning outcomes. By incorporating the service-dominant logic perspective into the research model, we designed an online survey to investigate the impact of platform value co-creation on learners' engagement outcomes. By employing a partial least squares-structural equation modeling (PLS-SEM), the results show that behavioral engagement, cognitive engagement, and emotional engagement have a significant impact on learning outcomes through the mediating effect of platform value, a second-order hierarchical latent variable. This study has multiple theoretical contributions and practical implications. First, we found new evidence that pursuing good learning outcomes in a non-formal online learning setting is not just a technological architecture or pedagogic guidelines, but also a "win-win" value co-creation process. Second, our results confirm the posited direct and indirect effects, thus evidencing functional value, emotional value, social value, and personalized value as components of the platform value construct, and it as a driver and mediator for better online learning outcomes. Third, our results underscore the importance of platform value in studying the impact of learner engagement on learning outcomes and provide a sharper theoretical lens to evaluate online learning platform value from the perspective of online learners.

Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study.

BACKGROUND: It is believed that artificial intelligence (AI) will be an integral part of health care services in the near future and will be incorporated into several aspects of clinical care such as prognosis, diagnostics, and care planning. Thus, many technology companies have invested in producing AI clinical applications. Patients are one of the most important beneficiaries who potentially interact with these technologies and applications; thus, patients' perceptions may affect the widespread use of clinical AI. Patients should be ensured that AI clinical applications will not harm them, and that they will instead benefit from using AI technology for health care purposes. Although human-AI interaction can enhance health care outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. OBJECTIVE: The main objective of this study was to examine how potential users (patients) perceive the benefits, risks, and use of AI clinical applications for their health care purposes and how their perceptions may be different if faced with three health care service encounter scenarios. METHODS: We designed a 2×3 experiment that crossed a type of health condition (ie, acute or chronic) with three different types of clinical encounters between patients and physicians (ie, AI clinical applications as substituting technology, AI clinical applications as augmenting technology, and no AI as a traditional in-person visit). We used an online survey to collect data from 634 individuals in the United States. RESULTS: The interactions between the types of health care service encounters and health conditions significantly influenced individuals' perceptions of privacy concerns, trust issues, communication barriers, concerns about transparency in regulatory standards, liability risks, benefits, and intention to use across the six scenarios. We found no significant differences among scenarios regarding perceptions of performance risk and social biases. CONCLUSIONS: The results imply that incompatibility with instrumental, technical, ethical, or regulatory values can be a reason for rejecting AI applications in health care. Thus, there are still various risks associated with implementing AI applications in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also evident if the AI applications are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies are needed to identify the challenges that may raise concerns for implementing and using AI applications. This study could provide researchers and managers with critical insights into the determinants of individuals' intention to use AI clinical applications. Regulatory agencies should establish normative standards and evaluation guidelines for implementing AI in health care in cooperation with health care institutions. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI clinical applications.

Do Hospitals Need to Extend Telehealth Services? An Experimental Study of Different Telehealth Modalities during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic has changed health care systems and clinical workflows in many countries, including the United States. This public health crisis has accelerated the transformation of health care delivery through the use of telehealth. Due to the coronavirus' severity and pathogenicity, telehealth services are considered the best platforms to meet suddenly increased patient care demands, reduce the transformation of the virus, and protect patients and health care workers. However, many hospitals, clinicians, and patients are not ready to switch to virtual care completely. OBJECTIVES: We designed six experiments to examine how people (as an actual beneficiary of telehealth) evaluate five telehealth encounters versus face-to-face visits. METHODS: We used an online survey to collect data from 751 individuals (patients) in the United States. RESULTS: Findings demonstrate that significant factors for evaluating five types of telehealth encounters are perceived convenience expected from telehealth encounters, perceived psychological risks associated with telehealth programs, and perceived attentive care services delivered by telehealth platforms. However, significant elements for comparing telehealth services with traditional face-to-face clinic visits are perceived cost-saving, perceived time-saving, perceived hygienic services, perceived technical errors, perceived information completeness, perceived communication barriers, perceived trust in medical care platforms' competency, and perceived privacy concerns. CONCLUSION: Although the in-person visit was reported as the most preferred care practice, there was no significant difference between people's willingness to use face-to-face visits versus virtual care. Nevertheless, before the widespread rollout of telehealth platforms, health care systems need to determine and address the challenges of implementing virtual care to improve patient engagement in telehealth services. This study also provides practical implications for health care providers to deploy telehealth effectively during the pandemic and postpandemic phases.

Using Electronic Health Records to Mitigate Workplace Burnout Among Clinicians During the COVID-19 Pandemic: Field Study in Iran.

BACKGROUND: The COVID-19 pandemic spread worldwide in 2020. Notably, in the countries dealing with massive casualties, clinicians have worked in new conditions characterized by a heavy workload and a high risk of being infected. The issue of clinician burnout during the pandemic has attracted considerable attention in health care research. Electronic health records (EHRs) provide health care workers with several features to meet a health system's clinical needs. OBJECTIVE: We aim to examine how the use of EHR features affects the burnout of clinicians working in hospitals that have special wards for confirmed COVID-19 cases. METHODS: Using an online survey, we collected data from 368 physicians, physician assistants, and nurses working in six hospitals that have implemented EHRs in the city of Tehran in Iran. We used logistic regression to assess the association between burnout and awareness of EHR features, EHR system usability, concerns about COVID-19, technology solutions, hospital technology interventions, hospital preparedness, and professional efficacy adjusted for demographic and practice characteristics. RESULTS: The primary outcome of our study was self-reported burnout during the COVID-19 pandemic. Of the 368 respondents, 36% (n=134) reported having at least one symptom of burnout. Participants indicated that the leading cause of EHR-related stress is inadequate training for using technology (n=159, 43%), followed by having less face-to-face time with patients (n=140, 38%). Positive perceptions about the EHR's ease of use were associated with lower odds of burnout symptoms. More interventions, such as clear communication of regulations; transparency in policies, expectations, and goals regarding the use of technology in the clinical workflow; and hospital preparedness to cope with the challenges of the pandemic, were associated with lower odds of burnout. CONCLUSIONS: The use of EHR applications, hospital pandemic preparation programs, and transparent technology-related policies and procedures throughout the epidemic can be substantial mitigators of technology-based stress and clinician burnout. Hospitals will then be better positioned to devise or modify technology-related policies and procedures to support physicians' and nurses' well-being during the COVID-19 pandemic. Training programs, transparency in communications of regulations, and developing a clear channel for informing clinicians of changes in policies may help reduce burnout symptoms among physicians and nurses during a pandemic. Providing easily accessible mentorship through teleconsultation and 24-hour available information technology support may also help to mitigate the odds of burnout.

The impact of data entry structures on perceptions of individuals with chronic mental disorders and physical diseases towards health information sharing.

BACKGROUND AND OBJECTIVE: Collecting, integrating, and sharing mental and physical health information can enhance the care process of patients and improve the completeness of patient databases in the health information exchange (HIE) networks. There is a need to encourage patients with physical and mental disorders to share their health information with providers. Data entry interfaces are suggested as an important factor affecting the quality of information. However, little is known about whether individuals with different diseases (mental and physical) care for the data entry structure in sharing personal health information (PHI). MATERIALS AND METHODS: We conduct four experiments to examine the impact of different health problems (mental vs. physical) and types of data entry interfaces (structured vs. unstructured) on individuals' perceptions of information quality and their willingness to share their health information. RESULTS: Findings demonstrate that the type of disease and degree of data entry structure significantly influence individuals' perceptions of usefulness, accessibility, concise presentation, understandability, psychological risk, privacy concern, stigma, and willingness to share health information. DISCUSSION: People with mental disorders prefer structured data interfaces as they perceive that a high degree of data entry structure can protect their privacy and mitigate stigma and psychological risk more than unstructured interfaces. Individuals with physical illnesses favor structured interfaces for their format, which is brief, comprehensive, accessible, useful, and understandable. People suffering from physical diseases are more likely to share their information when a highly-structured data entry interface is used. Moreover, individuals with mental disorders are less likely to disclose their information when providers collect health records using an unstructured data entry interface. CONCLUSIONS: This study suggests that the best level of structure for data entry interfaces could be designed at the point of care consistent with patients' health status and their type of diseases to improve the success of HIE networks.

Revisiting Effective Communication Between Patients and Physicians: Cross-Sectional Questionnaire Study Comparing Text-Based Electronic Versus Face-to-Face Communication.

BACKGROUND: Research has shown that text-based communication via telemedicine will continue to be a mode of communication that patients and physicians use in the future. However, very few studies have examined patients' perspectives regarding the increased use of text-based communication versus face-to-face (FtF) communication. OBJECTIVE: This study aimed to understand and compare the potential differences in patients' perceptions of communication effectiveness with their physicians through different modes of communication. METHODS: We conducted a web-based survey of 345 patients to explore the impact of different channels on effective communication and perceived health behavior and outcomes. We tested the impact of patients' perceived communication and media effectiveness on their self-efficacy, communication satisfaction, and perceived health outcomes, separately for text-based information technology (IT)-mediated communication and FtF communication. Furthermore, we conducted a group comparison to identify significant differences across these 2 groups. RESULTS: We found no significant differences between patients' perceptions of effective communication using either IT-mediated communication or FtF communication with their physicians. However, we found significant differences in patients' perception of media effectiveness: patients perceived FtF communication to be a more favorable medium (P=.02). Interestingly, we found no significant difference in terms of benefits (P=.09) and success (P=.08) of IT-mediated communication versus FtF communication. CONCLUSIONS: The results of this study imply that patients can achieve the same level of communication effectiveness with their physicians using IT-mediated communication as they would in comparable FtF interactions, but patients view FtF communication to be a more favorable medium than IT-mediated communication.

The effects of data entry structure on patients' perceptions of information quality in Health Information Exchange (HIE).

BACKGROUND AND OBJECTIVE: To exchange patient health information using Health Information Exchange (HIE) projects, such information first should be collected thoroughly using an appropriate data entry interface that reinforces information quality (IQ). Assessment of the given data interface based on its structure level may give us a better understanding of patients' attitudes toward information-sharing efforts. The main objective of this study is to examine the effects of data structure on perceptions and attitudes of patients toward the quality of health information that may be shared through HIE networks. MATERIALS AND METHODS: Eight experiments were conducted to examine the impact of different design of information collection interfaces (structured vs. unstructured) to record two types of health information (sensitive vs. non-sensitive) that can be used for two types of sharing purposes (health care vs. marketing). RESULTS: Results show that the degree of data entry structure can significantly influence patients' perceptions of completeness, accuracy, psychological risk, accessibility of data, concise representation, and understandability of health information. DISCUSSION: There is a connection between data entry interface design and patients' perceptions of the quality of health information used in HIE networks, which in turn, could lead to the development of best practices in interface design and data collection techniques. This may also improve interactions between patients and healthcare entities, enhance patients' attitudes toward data collection procedures and HIE, and help healthcare providers use complete and accurate databases. CONCLUSIONS: We propose that healthcare professionals can tailor data entry interfaces based on the sensitivity of medical data and the purpose of information exchange.

The Potential of Blockchain Technology for Health Information Exchange: Experimental Study From Patients' Perspectives.

BACKGROUND: Nowadays, a number of mechanisms and tools are being used by health care organizations and physicians to electronically exchange the personal health information of patients. The main objectives of different methods of health information exchange (HIE) are to reduce health care costs, minimize medical errors, and improve the coordination of interorganizational information exchange across health care entities. The main challenges associated with the common HIE systems are privacy concerns, security risks, low visibility of system transparency, and lack of patient control. Blockchain technology is likely to disrupt the current information exchange models utilized in the health care industry. OBJECTIVE: Little is known about patients' perceptions and attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the main HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, this study aimed at exploring the core value of blockchain technology in the health care industry from health care consumers' views. METHODS: To recognize the potential applications of blockchain technology in health care practices, we designed 16 information exchange scenarios for controlled Web-based experiments. Overall, 2013 respondents participated in 16 Web-based experiments. Each experiment described an information exchange condition characterized by 4 exchange mechanisms (ie, direct, lookup, patient-centered, and blockchain), 2 types of health information (ie, sensitive vs nonsensitive), and 2 types of privacy policy (weak vs strong). RESULTS: The findings show that there are significant differences in patients' perceptions of various exchange mechanisms with regard to patient privacy concern, trust in competency and integrity, opt-in intention, and willingness to share information. Interestingly, participants hold a favorable attitude toward the implementation of blockchain-based exchange mechanisms for privacy protection, coordination, and information exchange purposes. This study proposed the potentials and limitations of a blockchain-based attempt in the HIE context. CONCLUSIONS: The results of this research should be of interest to both academics and practitioners. The findings propose potential limitations of a blockchain-based HIE that should be addressed by health care organizations to exchange personal health information in a secure and private manner. This study can contribute to the research in the blockchain area and enrich the literature on the use of blockchain in HIE efforts. Practitioners can also identify how to leverage the benefit of blockchain to promote HIE initiatives nationwide.

Comparison of consumers' perspectives on different health information exchange (HIE) mechanisms: an experimental study.

OBJECTIVE: Consumers' willingness to allow the distribution of their health data is a prerequisite for the success of any health information exchange (HIE) initiative. Several mechanisms are being used by healthcare organizations to exchange health information electronically. Our goal is to investigate how patients' preferences regarding information exchange (i.e., privacy concern, opt-in intention, and perceived health information sensitivity) are affected by different HIE models and exchange architectures. METHODS: We designed a series of scenarios for controlled online experiments. Totally, 1416 respondents participated in seven experiments. Each experiment includes a separate scenario and 27 questions to measure outcome variables and demographics. Data were collected from a wide range of adult population in the U.S. and all the experiments were performed in English. RESULTS: The findings demonstrate that there are significant differences in patients' perceptions of different HIE mechanisms in terms of privacy concern and opt-in intention. Consumers believe that the patient-controlled HIE at the regional level is the most preferred model to protect health information privacy and they are willing to opt-in to this model. However, there is no solid evidence to show that different HIE mechanisms would affect the consumers' perceived health information sensitivity. CONCLUSIONS: This study supports the importance of patient-controlled HIE models that potentially enable patients to access, manage, integrate, and share their medical information with multiple healthcare organizations. The results show in-depth consumer adoption patterns across multiple HIE models and architectures which are required to identify optimal approaches for HIE implementation using different exchange mechanisms.