Relevant quote:
After the pilot period, Garcia and the team issued a survey to the clinicians, asking them to report on their experience. They reported that the AI-generated drafts lightened the cognitive load of responding to patient messages and improved their feelings of work exhaustion despite objective findings that the drafts did not save the clinicians’ time. That’s still a win, Garcia said, as this tool is likely to have even broader applicability and impact as it evolves.
Link to paper in JAMA (currently open access)
I am in complete agreement. I am a data scientist in health care and over my career I’ve worked on very few ML/AI models, none of which were generative AI or LLM based. I’ve worked on so few because nine times out of ten I am arguing against the inclusion of ML/AI because there are better solutions involving simpler tech. I have serious concerns about ethics when it comes to automating just about anything in patient care, especially when it can effect population health or health equity. However, this was one of the only uses I’ve seen for a generative AI in healthcare where it showed actual promise for being useful, and wanted to share it.