Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations.

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges.

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.

Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study.

BACKGROUND: Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform. OBJECTIVE: This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter. METHODS: Data were collected from 329 Twitter users in the United States using a web-based survey. RESULTS: Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (ß=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (ß=-.38; P<.001) and specific health information (ß=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter. CONCLUSIONS: The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey.

BACKGROUND: In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors. OBJECTIVE: This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption. METHODS: Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives. RESULTS: The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives. CONCLUSIONS: The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

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.

Adoption and use of various health information exchange methods for sending inside health information in US hospitals.

BACKGROUND: Hospitals may adopt various information transmission methods to participate in health information exchange (HIE) programs. However, even if they adopt multiple mechanisms, they may not actively use all of them to send inside information. This study analyzes the frequently used methods for sending data and the common barriers blocking sending practices in hospital settings. METHODS: Our sample included 3,015 community hospitals that reported their methods of sending patient health information in the 2019 American Hospital Association Information Technology Supplement Survey. The relationship between obstacles hospitals experienced and their use of the information-sending method was analyzed by using robust Poisson regression models. RESULTS: Many-to-many exchanges that involve intermediaries such as a health information service provider (HISP), electronic health record (EHR) vendor-based network, and national network, once adopted, were more often used by hospitals than one-to-one exchange methods such as provider portals and direct access to EHR by login credentials. Hospitals that lacked the technical capability to electronically send patient health information were less likely to use any of the methods (p <.01), while hospitals located in a more concentrated market were more likely to send information to outside providers by using provider portal, interface connection and national network (p <.01). DISCUSSION: There is still a notable gap between hospitals' adoption and the actual use of different HIE methods to send inside information to outside organizations. Results argue that even if hospitals adopted an HIE method, they might not necessarily participate in the actual sharing of information, and the method may remain unused due to several usage barriers. CONCLUSION: Hospital and market-level barriers associated with using one-to-one and many-to-many HIE methods for sharing information may affect progress in interoperability. Examining the barriers to using multiple HIE methods and their impact on interoperability could offer implications for health information technology (IT) policy and inform health system leaders.

Evolution of Health Information Sharing Between Health Care Organizations: Potential of Nonfungible Tokens.

This study attempts to explain the development and progress of the technology used for sharing health information across health care organizations (such as hospitals and physicians' offices). First, we describe the strengths and weaknesses of traditional sharing models, health information exchange (HIE), and blockchain-based HIE. Second, the potential use of nonfungible token (NFT) protocols in HIE models is proposed as the next possible move for information-sharing initiatives in health care. In addition to some potential opportunities and distinguishing features (eg, ownability, verifiability, and incentivization), we identify the uncertainty and risks associated with the application of NFTs, such as the lack of a dedicated regulatory framework for legal ownership of digital patient data. This paper is among the first to discuss the potential of NFTs in health care. The use of NFTs in HIE networks could generate a new stream of research for future studies. This study provides practical insights into how the technological foundations of information-sharing efforts in health care have developed and diversified from earlier forms.

Benefits and concerns associated with blockchain-based health information exchange (HIE): a qualitative study from physicians' perspectives.

BACKGROUND: Blockchain technology has the potential to revolutionize information sharing in companies. Many studies suggest using blockchain-powered platforms to replace existing mechanisms for health information exchange (HIE) across healthcare organizations. However, very few blockchain-based projects have been implemented in the healthcare sector. This study takes a qualitative approach to explore benefits, concerns, and barriers to the rollout of blockchain in HIE projects from physicians' perspectives. METHODS: The Promoting Action on Research Implementation in Health Services (PARIHS) framework was used to help us better understand root causes, existing problems, perceived risks, perceived benefits, and suggestions. In-depth interviews have been conducted with 38 physicians in six months. The data were analyzed and coded using NVIVO to classify conceptually similar themes mentioned by the interviewees. RESULTS: In total, seven themes have been identified. The key benefits are categorized into three themes: innovative technological features, collaborative ecosystem, and system performance. The main concerns and risks are categorized into four themes: individual, organizational, technological, and market-related issues. The findings can contribute to knowledge by highlighting key values expected from blockchain technology in HIEs. The results also explore obstacles to leveraging the blockchain in healthcare from the perspectives of an important stakeholder (physicians). CONCLUSIONS: The results show that although blockchain technology may create several benefits (e.g., innovative technological features, collaborative ecosystem, and system performance), its applications in healthcare are still in their early stages. The perceptions of the individual issues (e.g., lack of knowledge), organizational issues (e.g., implementation issues), technological issues (e.g., blockchain model types), and market-related issues (e.g., regulatory concerns) indicate that blockchain-based applications in healthcare continue to be an emerging field. This study has practical implications as understanding these concerns can help developers and healthcare managers identify potential issues in the planning, developing, and implementing blockchain-based HIE systems. Addressing these barriers would support the widespread use of blockchain-based HIEs in different healthcare settings and facilitate interoperability and connectivity in regional and community health information networks.

Public concerns and burdens associated with face mask-wearing: Lessons learned from the COVID-19 pandemic.

This study attempts to identify and categorize the key concerns of wearing masks. An online survey was used to collect data from 2746 people in the United States. Results show that the mask-wearing concerns can be classified into three categories; discomfort barriers (physical discomfort and communication discomfort), external factors (overstated news about coronavirus threat, political beliefs, and absence of mask-wearing culture), and usability issues (lack of effectiveness, unnecessariness of masks in certain cases, and mask maintenance issues). The findings demonstrate that all mentioned concerns strongly shape people's attitudes toward wearing masks, except for political beliefs and lack of effectiveness.

The Influence of Gamification and Information Technology Identity on Postadoption Behaviors of Health and Fitness App Users: Empirical Study in the United States.

BACKGROUND: The use of health and fitness apps has been on the rise to monitor personal fitness and health parameters. However, recent research discovered that many users discontinue using these apps after only a few months. Gamification has been suggested as a technique to increase users' interactions with apps. Nevertheless, it is still not clear how gamification mechanisms encourage continued use and inspire user self-management. OBJECTIVE: The main objective of this study was to articulate how gamification mechanisms in studies of designing and using health and fitness apps can contribute to the realization of information technology (IT) identity and positive behavioral outcomes. The broader goal was to shed light on how gamification mechanisms will translate into positive use behaviors in the context of mobile health apps. METHODS: Data were collected from 364 users of health and fitness apps through an online survey to empirically examine the proposed model. RESULTS: Based on identity theories, this study suggests the fully mediating role of IT identity to describe how gamification elements can lead to continued intention to use health and fitness apps, and increase users' tendency for information sharing through the apps. The findings indicate that perceived gamification can increase users' IT identity. In turn, a higher IT identity would encourage users to continue using the apps and share more personal health information with others through the apps. CONCLUSIONS: The results of this study can have practical implications for app designers to use gamification elements to increase users' dependency, relatedness, and emotional energy associated with health apps. Moreover, the findings can have theoretical contributions for researchers to help better articulate the process in which gamification can be translated into positive use behaviors.

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 Role of Information Technology Mindfulness in the Postadoption Stage of Using Personal Health Devices: Cross-Sectional Questionnaire Study in Mobile Health.

BACKGROUND: Although personal health devices (for example, smartwatches, fitness trackers and intelligent bracelets) offer great potential to monitor personal fitness and health parameters, many users discontinue using them after a few months. Thus, it is critical to study the postadoption behaviors of current users to enhance their engagement with personal health devices and use behaviors. However, there is little empirical research on the factors affecting users' engagement in beneficial use behaviors. Mindfulness and identity are not new topics, but the applications of these concepts in the field of information systems are emerging themes. Information technology (IT) mindfulness has been conceptualized in previous studies; however, little is known about the antecedents and consequences of IT mindfulness in the mobile health (mHealth) context. OBJECTIVE: The main aim of this study is to explore both IT identity and IT mindfulness to develop a new ground for research in the domain of mHealth postadoption. Thus, we aim to explain why users should be fully mindful of their engagement with PHDs and what could be the consequences and implications. METHODS: This study proposes that IT mindfulness can play an important role in improving the use behaviors of users. Through a web-based survey with 450 current users of a personal health device, this paper tests the relationship between IT identity and IT mindfulness in the postadoption stage of using personal health devices. RESULTS: We found that IT identity significantly shapes IT mindfulness associated with PHDs. Moreover, the IT identity-IT mindfulness relationship is negatively moderated by individuals' perceived health status (P=.003). Finally, the results of this study show that IT mindfulness can significantly predict automatic use behaviors (eg, continued intention to use), active use behaviors (eg, feature use and enhanced use behaviors), and commitment behaviors in using personal health devices (eg, positive word-of-mouth intention). CONCLUSIONS: The findings of this study provide implications for both research and practice. This study can contribute to our current understanding of IT mindfulness by developing and empirically testing a research model that explains the determinants and outcomes of the IT mindfulness construct in the context of personal health devices. The results imply that IT mindfulness significantly helps individuals express their alertness, awareness, openness, and orientation in the present in their postadoption interactions with smart devices used for health care purposes. Finally, our findings may assist practitioners and IT developers in designing mindfulness-supporting PHDs. Owing to the impact of IT mindfulness on postadoption behaviors, its 4 dimensions could be used for developing PHD technologies. Moreover, PHD developers may need to direct their efforts toward increasing IT mindfulness by reinforcing IT identity to serve and retain a wide range of target users.

Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives.

BACKGROUND: Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. METHODS: We develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers' perspectives. We use an online survey to collect data from 307 individuals in the United States. RESULTS: The proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs. CONCLUSIONS: This study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.

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.

Patients' Perceptions of Different Information Exchange Mechanisms: An Exploratory Study in the United States.

BACKGROUND: Patients may seek health care services from various providers during treatment. These providers could serve in a network (affiliated) or practice separately (unaffiliated). Thus, using secure and reliable health information exchange (HIE) mechanisms would be critical to transfer sensitive personal health information (PHI) across distances. Studying patients' perceptions and opinions about exchange mechanisms could help health care providers build more complete HIEs' databases and develop robust privacy policies, consent processes, and patient education programs. OBJECTIVES: Due to the exploratory nature of this study, we aim to shed more light on public perspectives (benefits, concerns, and risks) associated with the four data exchange practices in the health care sector. METHODS: In this study, we compared public perceptions and expectations regarding four common types of exchange mechanisms used in the United States (i.e., traditional, direct, query-based, patient-mediated exchange mechanisms). Traditional is an exchange through fax, paper mailing, or phone calls, direct is a provider-to-provider exchange, query-based is sharing patient data with a central repository, and patient-mediated is an exchange mechanism in which patients can access data and monitor sharing. Data were collected from 1,624 subjects using an online survey to examine the benefits, risks, and concerns associated with the four exchange mechanisms from patients' perspectives. RESULTS: Findings indicate that several concerns and risks such as privacy concerns, security risks, trust issues, and psychological risks are raised. Besides, multiple benefits such as access to complete information, communication improvement, timely and convenient information sharing, cost-saving, and medical error reduction are highlighted by respondents. Through consideration of all risks and benefits associated with the four exchange mechanisms, the direct HIE mechanism was selected by respondents as the most preferred mechanism of information exchange among providers. More than half of the respondents (56.18%) stated that overall they favored direct exchange over the other mechanisms. 42.70% of respondents expected to be more likely to share their PHI with health care providers who implemented and utilized a direct exchange mechanism. 43.26% of respondents believed that they would support health care providers to leverage a direct HIE mechanism for sharing their PHI with other providers. The results exhibit that individuals expect greater benefits and fewer adverse effects from direct HIE among health care providers. Overall, the general public sentiment is more in favor of direct data transfer. Our results highlight that greater public trust in exchange mechanisms is required, and information privacy and security risks must be addressed before the widespread implementation of such mechanisms. CONCLUSION: This exploratory study's findings could be interesting for health care providers and HIE policymakers to analyze how consumers perceive the current exchange mechanisms, what concerns should be addressed, and how the exchange mechanisms could be modified to meet consumers' needs.

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 Impacts of the Perceived Transparency of Privacy Policies and Trust in Providers for Building Trust in Health Information Exchange: Empirical Study.

BACKGROUND: In the context of exchange technologies, such as health information exchange (HIE), existing technology acceptance theories should be expanded to consider not only the cognitive beliefs resulting in adoption behavior but also the affect provoked by the sharing nature of the technology. OBJECTIVE: We aimed to study HIE adoption using a trust-centered model. Based on the Theory of Reasoned Action, the technology adoption literature, and the trust transfer mechanism, we theoretically explained and empirically tested the impacts of the perceived transparency of privacy policy and trust in health care providers on cognitive and emotional trust in an HIE. Moreover, we analyzed the effects of cognitive and emotional trust on the intention to opt in to the HIE and willingness to disclose health information. METHODS: A Web-based survey was conducted using data from a sample of 493 individuals who were aware of the HIE through experiences with a (or multiple) provider(s) participating in an HIE network. RESULTS: Structural Equation Modeling analysis results provided empirical support for the proposed model. Our findings indicated that when patients trust in health care providers, and they are aware of HIE security measures, HIE sharing procedures, and privacy terms, they feel more in control, more assured, and less at risk. Moreover, trust in providers has a significant moderating effect on building trust in HIE efforts (P<.05). Results also showed that patient trust in HIE may take the forms of opt-in intentions to HIE and patients' willingness to disclose health information that are exchanged through the HIE (P<.001). CONCLUSIONS: The results of this research should be of interest to both academics and practitioners. The findings provide an in-depth dimension of the HIE privacy policy that should be addressed by the health care organizations to exchange personal health information in a secure and private manner. This study can contribute to trust transfer theory and enrich the literature on HIE efforts. Primary and secondary care providers can also identify how to leverage the benefit of patients' trust and trust transfer process to promote HIE initiatives nationwide.

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.