Read the Atlantic article with my gift link above ^^
Generative AI in the classroom:
- Cheating on take-home exams (chatbot will answer any exam question)
- Cheating on in-class discussion (chatbot in real-time presents excellent discussion points on any topic)
- Cheating in debate competition (chatbot helps teams prepare a rebuttal between tournament rounds)
- The risk: that class on European History is actually a class on “How to copy and paste answers from AI” and no learning is achieved.
The rare positive story from the education field shows us a glimmer of hope. A professor assigns a homework task that explicitly asks for the student to use generative AI to create a first draft, and then to use the draft to write a critique of the AI-written document, demonstrating command of the material and ability to critique others’ work.
Generative AI (I’ll abbreviate Gen-AI) in healthcare:
- Gen-AI composes an excellent progress note summarizing a physician and patient conversation, within seconds of the end of the visit, reducing physician cognitive and time burden
- Gen-AI helps document more diagnoses and perhaps more accurately because it is captured and generated within seconds of a visit and not hours or weeks later when physician memory fades
- Gen-AI replies to patient online questions by drafting a reasonable reply based on prior EHR (electronic health record) data, to reduce nurse and physician typing burden
- Gen-AI helps summarize hundreds of pages of medical records to speed up nurse and physician work as they meet new patients with years of data
So far so good. These are all win-win scenarios: doctors and nurses work more quickly and easily, patients get better care.
It gets touchy:
- Gen-AI helps doctors prepare “prior authorization” documents to advocate for patients getting insurers to pay for treatments. This is directly opposed by Gen-AI helping insurers deny these requests. This is a no-win situation.
- Gen-AI helps doctors generate higher quality, more complete notes that show that complex care was provided to the patient, possibly improving reimbursement. This is directly opposed by Gen-AI helping insurers spot such changes. Another no-win situation.
None of the healthcare examples elicit from me any sense of “cheating” as for high school or college students. But it is clear that this new “Gen-AI” entity is changing the conversation.
Depending on the context, Gen-AI is a powerful ally to improve healthcare. At other times, Gen-AI is a no-win arms race that sucks up expensive electrical power on both sides and the battle lines don’t move.
CMIO’s take?
Where can we turn the generative AI conversation from backward-thinking no-win situations to lateral-thinking win-win conversations? The first category is pure waste. The second is much harder and much more important. This is the struggle CMIO’s and our analogues in other fields must take on.
