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BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive, incurable fibrotic lung disease in which patients and caregivers report a high symptom burden. Symptoms are often poorly managed and patients and caregivers struggle to alleviate their distress in the absence of self-management support.AimTo explore perceptions of symptoms, symptom management strategies and self-efficacy for patients with IPF and caregivers who received self-management education and action plans created and provided in a Multidisciplinary Collaborative Interstitial Lung Disease (MDC-ILD) Clinic.DesignA qualitative study was conducted with participants recruited from the MDC-ILD Clinic. Participants received an early integrated palliative approach; most attended ILD pulmonary rehabilitation and some received home care support. Semistructured interviews were conducted. Patient participants completed Measure Yourself Medical Outcome Profile (MYMOP) for symptom assessment and Chronic Obstructive Pulmonary Disease Self-Efficacy Scale to assess self-management efficacy.ResultsThirteen patients and eight self-declared caregiver participants were interviewed. IPF severity ranged from mild to advanced disease. Participants integrated and personalised self-management strategies. They were intentional and confident, focused on living well and engaged in anticipatory planning. Twelve participants completed the MYMOP. Five reported dyspnoea. Four reported fatigue as an additional or only symptom. One reported cough. Five declared no dyspnoea, cough or fatigue. Participants reported 80% self-efficacy in symptom management.ConclusionsThe approach to symptom self-management and education was beneficial to patients with IPF and caregiver participants. Participants personalised the strategies, focusing on living, and planned both in the moment and for the future. They were confident and expressed dignity and meaning in their lives.

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.

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

Introduction: Despite the support for and benefits of data-driven learning, physician engagement is variable. This study explores systemic influences of physician use of data for performance improvement in continuing professional development (CPD) by analyzing and interpreting data sources from organizational and institutional documents. Methods: The document analysis is the third phase of a mixed-methods explanatory sequential study examining cultural factors that influence data-informed learning. A gray literature search was conducted for organizations both in Canada and the United States. The analysis contains nonparticipant observations from professional learning bodies and medical specialty organizations with established roles within the CPD community known to lead and influence change in CPD. Results: Sixty-two documents were collected from 20 Canadian and American organizations. The content analysis identified the following: (1) a need to advocate for data-informed self-assessment and team-based learning strategies; (2) privacy and confidentiality concerns intersect at the point of patient data collection and physician-generated outcomes and need to be acknowledged; (3) a nuanced data strategy approach for each medical specialty is needed. Discussion: This analysis broadens our understanding of system-level factors that influence the extent to which health information custodians and physicians are motivated to engage with data for learning.