Accelerating AI Innovation in Healthcare Through Mentorship.

The adoption of Artificial Intelligence (AI) in the Canadian healthcare system falls behind that of other countries. Socio-technological considerations such as organizational readiness and a limited understanding of the technology are a few barriers impeding its adoption. To address this need, this study implemented a five-month AI mentorship program with the primary objective of developing participants' AI toolset. The analysis of our program's effectiveness resulted in recommendations for a successful mentorship and AI development and implementation program. 12 innovators and 11 experts from diverse backgrounds were formally matched and two symposiums were integrated into the program design. 8 interviewed participants revealed positive perceptions of the program underscoring its contribution to their professional development. Recommendations for future programs include: (1) obtaining organizational commitment for each participant; (2) incorporating structural supports throughout the program; and (3) adopting a team-based mentorship approach. The findings of this study offer a foundation rooted in evidence for the formulation of policies necessary to promote the integration of AI in Canada.

Paving the Way Forward for Evidence-Based Continuing Professional Development.

ABSTRACT: Continuing professional development (CPD) fosters lifelong learning and enables health care providers to keep their knowledge and skills current with rapidly evolving health care practices. Instructional methods promoting critical thinking and decision making contribute to effective CPD interventions. The delivery methods influence the uptake of content and the resulting changes in knowledge, skills, attitudes, and behavior. Educational approaches are needed to ensure that CPD meets the changing needs of health care providers. This article examines the development approach and key recommendations embedded in a CE Educator's toolkit created to evolve CPD practice and foster a learning experience that promotes self-awareness, self-reflection, competency, and behavioral change. The Knowledge-to-Action framework was used in designing the toolkit. The toolkit highlighted three intervention formats: facilitation of small group learning, case-based learning, and reflective learning. Strategies and guidelines to promote active learning principles in CPD activities within different modalities and learning contexts were included. The goal of the toolkit is to assist CPD providers to design educational activities that optimally support health care providers' self-reflection and knowledge translation into their clinical environment and contribute to practice improvement, thus achieving the outcomes of the quintuple aim.

Performance Data Advocacy for Continuing Professional Development in Health Professions.

ABSTRACT: Efforts to optimize continuing professional development (CPD) are ongoing and include advocacy for the use of clinician performance data. Several educational and quality-based frameworks support the use of performance data to achieve intended improvement outcomes. Although intuitively appealing, the role of performance data for CPD has been uncertain and its utility mainly assumed. In this Scholarly Perspective, the authors briefly review and trace arguments that have led to the conclusion that performance data are essential for CPD. In addition, they summarize and synthesize a recent and ongoing research program exploring the relationship physicians have with performance data. They draw on Collins, Onwuegbuzie, and Johnson's legitimacy model and Dixon-Woods' integrative approach to generate inferences and ways of moving forward. This interpretive approach encourages questioning or raising of assumptions about related concepts and draws on the perspectives (i.e., interpretive work) of the research team to identify the most salient points to guide future work. The authors identify 6 stimuli for future programs of research intended to support broader and better integration of performance data for CPD. Their aims are to contribute to the discourse on data advocacy for CPD by linking conceptual, methodologic, and analytic processes and to stimulate discussion on how to proceed on the issue of performance data for CPD purposes. They hope to move the field from a discussion on the utility of data for CPD to deeper integration of relevant conceptual frameworks.

The Adoption of AI in Mental Health Care-Perspectives From Mental Health Professionals: Qualitative Descriptive Study.

BACKGROUND: Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. OBJECTIVE: A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. METHODS: A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. RESULTS: A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. CONCLUSIONS: AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

Elements and Definitions of Continuing Professional Development Leadership: A Scoping Review.

INTRODUCTION: Leaders are being asked to transform the way that continuing professional development (CPD) is delivered to focus on better, safer, and higher quality care. However, there is scarce literature on CPD leadership. We set out to study what CPD leadership means and describe the competencies required for CPD leadership. METHODS: A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-analyses extension guidelines for scoping reviews guidelines was conducted. With librarian support, four databases were searched for publications related to leadership, medical education, and CPD. Publications were screened by two reviewers and three reviewers extracted data. RESULTS: Among 3886 publications, 46 were eligible for a full-text review and 13 met the final inclusion criteria. There was no agreed upon definition of CPD leadership and variable models and approaches to leadership in the literature. Contextual issues shaping CPD (eg, funding, training, and information technology) are evolving. We identified several attitudes and behaviors (eg, strategic thinking), skills (eg, collaboration), and knowledge (eg, organizational awareness) important to CPD leadership, but no established set of unique competencies. DISCUSSION: These results offer the CPD community a foundation on which competencies, models, and training programs can build. This work suggests the need to build consensus on what CPD leadership means, what CPD leaders do, and what they will need to create and sustain change. We suggest the adaptation of existing leadership frameworks to a CPD context to better guide leadership and leadership development programs.

A Qualitative Study to Understand the Cultural Factors That Influence Clinical Data Use for Continuing Professional Development.

INTRODUCTION: The use of data to inform lifelong learning has become increasingly important in continuing professional development (CPD) practice. Despite the potential benefits of data-driven learning, our understanding of how physicians engage in data-informed learning activities, particularly for CPD, remains unclear and warrants further study. The purpose of this study was to explore how physicians perceive cultural factors (individual, organizational, and systemic) that influence the use of clinical data to inform lifelong learning and self-initiated CPD activities. METHODS: This qualitative study is part of an explanatory sequential mixed-methods study examining data-informed learning. Participants were psychiatrists and general surgeons from Canada and the United States. Recruitment occurred between April 2019 and November 2019, and the authors conducted semistructured telephone interviews between May 2019 and November 2019. The authors performed thematic analysis using an iterative, inductive method of constant comparative analysis. RESULTS: The authors interviewed 28 physicians: 17 psychiatrists (61%) and 11 general surgeons (39%). Three major themes emerged from the continuous, iterative analysis of interview transcripts: (1) a strong relationship between data and trust, (2) a team-based approach to data-informed learning for practice improvement, and (3) a need for organizational support and advocacy to put data into practice. CONCLUSION: Building trust, taking a team-based approach, and engaging multiple stakeholders, such as data specialists and organizational leadership, may significantly improve the use of data-informed learning. The results are situated in the existing literature, and opportunities for future research are summarized.

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.

BACKGROUND: As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. OBJECTIVE: With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness. METHODS: After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). RESULTS: Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. CONCLUSIONS: This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach.

BACKGROUND: Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today's health care providers. OBJECTIVE: The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS: To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS: The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS: Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940.

Best Practices for the Implementation and Sustainment of Virtual Health Information System Training: Qualitative Study.

BACKGROUND: The COVID-19 pandemic has necessitated the adoption and implementation of digital technologies to help transform the educational ecosystem and the delivery of care. OBJECTIVE: We sought to understand instructors' and learners' perceptions of the challenges and opportunities faced in implementing health information system virtual training amid the COVID-19 pandemic. METHODS: Semistructured interviews were conducted with education specialists and health care staff who provided or had taken part in a virtual instructor-led training at a large Canadian academic health sciences center. Guided by the Technology Acceptance Model and the Community of Inquiry framework, we analyzed interview transcript themes deductively and inductively. RESULTS: Of the 18 individuals participating in the study, 9 were education specialists, 5 were learners, 3 were program coordinators, and 1 was a senior manager at the Centre for Learning, Innovation, and Simulation. We found 3 predominant themes: adopting a learner-centered approach for a meaningful learning experience, embracing the advances in educational technologies to maximize the transfer of learning, and enhancing the virtual user experience. CONCLUSIONS: This study adds to the literature on designing and implementing virtual training in health care organizations by highlighting the importance of recognizing learners' needs and maximizing the transfer of learning. Findings from this study can be used to help inform the design and development of training strategies to support learners across an organization during the current climate and to ensure changes are sustainable.

An Education Framework for Effective Implementation of a Health Information System: Scoping Review.

BACKGROUND: To optimize their use of a new Health Information System (HIS), supporting health care providers require effective HIS education. Failure to provide this education can significantly hinder an organization's HIS implementation and sustainability efforts. OBJECTIVE: The aim of this review is to understand the most effective educational strategies and approaches to enable health care providers to optimally use an HIS. METHODS: Ovid MEDLINE, Ovid Embase, EBSCO Cumulative Index to Nursing and Allied Health Literature, and EBSCO Education Resources Information Center were searched to identify relevant papers. Relevant studies were systematically reviewed and analyzed using a qualitative thematic analysis approach. RESULTS: Of the 3539 studies screened, 17 were included for data extraction. The literature on the most effective approaches to enable health care providers to optimally use an HIS emphasized the importance of investing in engaging and understanding learners in the clinical context, maximizing the transfer of learning to care, and designing continuous and agile evaluation to meet the emerging demands of the clinical environment. CONCLUSIONS: This review supports the advancement of a new HIS learning framework that organizational leaders and educators can use to guide HIS education design and development. Future research should examine how this framework can be translated into practice.