Background: Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. Objective: As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. Methods: A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O’Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. Results: A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. Conclusions: The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients’ perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

Recent advancements in the field of Artificial Intelligence (AI) provide promising applications of this technology with the aim of solving complex healthcare challenges. These include optimizing operational efficiencies, supporting clinical administrative functions, and improving care outcomes. Numerous AI models are validated in research settings but few make their way into useful applications due to challenges associated with implementation and adoption. In this article, we describe some of these challenges, along with the need for a facilitating entity to safely translate AI systems into practical use. The authors propose a new AI governance framework to enable healthcare organizations with a mechanism to implement and adopt AI systems.

Virtual care extends beyond the walls of healthcare organizations to provide care at a distance. Although virtual care cannot be regarded as a solution for all health-related inquiries, it provides another care delivery channel for specific patient populations with appointments that do not require in-person physical examinations or procedures. A scoping review was conducted to define the meaning of virtual care, understand how virtual care has influenced the healthcare industry and is being expanded to complement the existing healthcare system, and describe the outcomes of using virtual care for patients and providers. Findings from the scoping review suggest that virtual care encompasses the provision of care using advanced video conferencing technology to support remote care that takes place between patients and providers and the use of virtual reality technology to simulate care environments. Some of virtual care’s use in healthcare includes application to pain and anxiety management, virtual consultations and follow-up visits, rehabilitation and therapy services, outpatient clinics, and emergency services. Lastly, from a provider and patient perspective, while both saw benefits of virtual care and scored the service relatively high on satisfaction after using virtual care, the greatest barrier to using virtual care may be technological challenges.

There are few studies that examine the processes that interdisciplinary teams engage in and how we can design health information systems (HIS) to support those team processes. This was an exploratory study with two purposes: (1) To develop a framework for interdisciplinary team communication based on structures, processes and outcomes that were identified as having occurred during weekly team meetings. (2) To use the framework to guide 'e-teams' HIS design to support interdisciplinary team meeting communication.

Asthma is one of the most common childhood illnesses. Guideline-driven clinical care positively affects patient outcomes for care. There are several asthma guidelines and reminder methods for implementation to help integrate them into clinical workflow. Our goal is to determine the most prevalent method of guideline implementation; establish which methods significantly improved clinical care; and identify the factors most commonly associated with a successful and sustainable implementation.

The purpose of this paper is to argue for an integration of cognitive and socio-technical approaches to assessing the impact of health information systems. Historically, health informatics research has examined the cognitive and socio-technical aspects of health information systems separately. In this paper we argue that evaluations of health information systems should consider aspects related to cognition as well as socio-technical aspects including impact on workflow (i.e. an integrated view). Using examples from the study of technology-induced error in healthcare, we argue for the use of simulations to evaluate the cognitive-socio-technical impacts of health information technology [36]. Implications of clinical simulations and analysis of cognitive-social-technical impacts are discussed within the context of the system development life cycle to improve health information system design, implementation and evaluation.