![]() Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. The articles cover diverse clinical settings and disciplines most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). Of the 9218 records retrieved, 45 (0.49%) articles were included. Data from the included articles were charted and summarized. Using Rayyan software, we screened titles and abstracts and selected full-text articles. The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?Ī scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. However, the development of AI applications does not guarantee their adoption into routine practice. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.Īrtificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires. The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. ![]() The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach-access, collaboration, and innovation. ![]() The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals. Care-management tools are typically utilized for chronic disease management. ![]()
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