The Potential and Perils of Generative Artificial Intelligence for Quality Improvement and Patient Safety

TL;DR

Generative AI has the potential to enhance healthcare quality and safety through automation of workflows and decision support, but careful implementation and scope management are essential to mitigate risks.

Contribution

This paper discusses the opportunities and challenges of applying generative AI in healthcare, emphasizing the importance of targeted, low-risk applications and implementation science.

Findings

GenAI can automate healthcare workflows at the point of care.

Small, fine-tuned models can provide explanations and evidence retrieval.

End-to-end clinical decision GenAI requires further research.

Abstract

Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.