Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement...

Area of Science:

• Healthcare technology
• Clinical informatics
• Artificial intelligence in medicine

Background:

• Machine learning (ML) solutions are complex sociotechnical systems increasingly used in healthcare.
• While promising for improving care, poor implementation can disrupt workflows, worsen inequities, and harm patients.
• ML applications present unique challenges due to model complexity, data biases, and poorly understood human impacts.

Purpose of the Study:

• To summarize current knowledge on implementing ML solutions in clinical care.
• To provide practical guidance for the safe, effective, and ethical deployment of ML in healthcare.
• To propose key questions for users considering ML solution deployment.

Main Methods:

• Literature review of ML implementation in clinical settings.
• Synthesis of knowledge on challenges and best practices.
• Development of a framework with three guiding questions for ML deployment.

Main Results:

• ML implementation involves data, models, infrastructure, and human interaction.
• Key challenges include model interpretability, data bias, and understanding behavioral impacts.
• Three critical questions address problem suitability, evaluation readiness, and optimal deployment/maintenance.

Conclusions:

• The Quality Improvement community is vital for safe and ethical ML translation.
• Systematic evaluation and thoughtful deployment are crucial for successful ML integration.
• Addressing ML's unique challenges is essential for realizing its potential in healthcare.