JMIR Publications

ABSTRACT

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 are transforming how medical conditions are being analyzed, diagnosed and treated. 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 skill sets for today’s health care providers.

Objective:

To accelerate the appropriate adoption of data driven and AI enhanced care by focusing on the mindset, skillet and toolset of point of care health providers and their leaders in the health system.

Methods:

To accelerate the adoption of AI and needed organizational change at a national level, our multi-stepped approach includes: 1) creating awareness and capacity building, 2) learning through innovation and adoption; 3) developing appropriate and strategic partnerships; 4) building effective knowledge exchange initiatives. Interventions designed to adapt knowledge to local context and overcome barriers to knowledge use include engagement activities to increase awareness, education 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 a scoping review are being conducted to understand the current state of AI education programs as reported in academic literature.

Results:

The environmental scan identified 24 AI accredited programs specific to health providers where 11 were from the USA, 6 from Canada, 4 from the UK, 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 with, and opportunities of, AI.

Conclusions:

Technologies are advancing more rapidly than organizations and professions can adopt and adapt 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.