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.

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.

Effect of ranolazine on glycaemic control in patients with type 2 diabetes treated with either glimepiride or metformin

AIM

To report the results of two phase III trials assessing the efficacy of ranolazine for glycaemic control in patients with type 2 diabetes on metformin or glimepiride background therapy.

METHODS

In two double-blind trials we randomized 431 and 442 patients with type 2 diabetes to ranolazine 1000 mg twice daily versus placebo added to either glimepiride (glimepiride add-on study) or metformin background therapy (metformin add-on study). Patients receiving ranolazine added to metformin had their metformin dose halved (with the addition of a metformin-matched placebo) relative to the placebo group to correct for a metformin-ranolazine pharmacokinetic interaction. The primary endpoint of the trials was the change from baseline in glycated haemoglobin (HbA1c) at week 24.

RESULTS

When added to glimepiride, ranolazine caused a 0.51% least squares mean [95% confidence interval (CI) 0.71, 0.32] decrease from baseline in HbA1c at 24 weeks relative to placebo and roughly doubled the proportion of patients achieving an HbA1c of <7% (27.1 vs 14.1%; p = 0.001). When added to metformin background therapy, there was no significant difference in the 24-week HbA1c change from baseline [placebo-corrected LS mean difference -0.11% (95% CI -0.31, 0.1)].

CONCLUSIONS

Compared with placebo, addition of ranolazine in patients with type 2 diabetes treated with glimepiride, but not metformin, significantly reduced HbA1c over 24 weeks. The decreased dose of metformin used in the metformin add-on study complicates the interpretation of this trial. Whether an effective regimen of ranolazine added to metformin for glycaemic control can be identified remains unclear.