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.
UNSTRUCTURED 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.
In interfacility transport care, a critical challenge exists in accurately matching ambulance response levels to patients' needs, often hindered by limited access to essential patient data at the time of transport requests. Existing systems cannot integrate patient data from sending hospitals' electronic health records (EHR) into the transfer request process, primarily due to privacy concerns, interoperability challenges, and the sensitive nature of EHR data. We introduce a distributed digital health platform, Interfacility Transport Care (ITC)-InfoChain, designed to solve this problem without compromising EHR security or data privacy.
A variety of Health Information Technology Systems (HITS) in the form of clinical information technology have gradually become established in the healthcare industry. Clinical information technology is considered as a strategic healthcare tool to improve the quality of health care service, the efficiency and effectiveness of healthcare professionals in the health care sector. Clinical Decision Support (CDS) systems are mainly used to assist healthcare professionals (such as physicians and specialists) in decision making and improving the quality of healthcare delivery. If CDS systems are not fully used by healthcare professionals, the effort and investment are doomed to failure. There are concerns regarding the adoption of CDS among healthcare professionals in Malaysia. However, factors affecting healthcare professionals’ adoption behavior related to using CDS are still not completely clear. The technology adoption models such as UTAUT are not specially targeted at healthcare professionals and they do not include the unique characteristics of healthcare professionals as well as special features and properties of CDS. The central characteristic of healthcare professionals that is considered in this research is professional autonomy. The special features and properties of CDS that are considered in this research are: 1- the level of knowledge codification and knowledge distribution and 2- guidelines and instructions generated by CDS and the level of interactivity between healthcare professionals and the CDS system.Integration of the healthcare professionals’ characteristics with features of CDS can provide a better understanding on IT adoption in the special context of healthcare practice. For this p rpose, the original version of the UTAUT has been extended with physician’s unique characteristics and special characteristics of CDS systems. This study thus proposes a research framework from a broader and an integrated perspective. To confirm the proposed framework 21 semi-structured interviews with some specialists (from different fields) were conducted in Malaysian hospitals. Furthermore, a survey has been used to evaluate the hypothesized model among 309 healthcare professionals in Malaysia. The structural equation model has been used to test the model in this context. The results stress the importance of perceived threat to professional autonomy, physicians involvement in decision making in CDS planning as well as implementation and also cognitive instrumental processes (mainly, usefulness perceptions) in determining physicians’ intention to use CDS systems. The empirical examination shows high predictive power for adoption intention and the influential role of these important variables. A recent study on usage of EMR based on the original UTAUT shows that the model can explain only 20% of the variance in the usage intention of EMR whereas the proposed model of this study can explain 47% of the variance of healthcare professionals’ behavioral intention in the CDS setting. The explanatory power of the proposed model indicates that the unique characteristics of physicians have a strong and statistically significant influence on physicians’ usage intention. This study adds to the body of knowledge on IT adoption models and sheds some new insights into technology acceptance models amongst healthcare professionals by finding unique factors affecting healthcare professional’s intention to accept the CDS system. Moreover, with this understanding, managers and practitioners are in a better position not only to identify the source of resistance toward the new CDS but also to devise strategies to improve the overall acceptance of the system among healthcare professionals in a hospital setting.
Abstract 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.
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.
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