Digital Pharmacist Digest - 🤖 The GPT revolution in medicine, evaluating equity in health algorithms, and more

Hiya 👋

We’re back with another edition of the digital pharmacist digest!

Here are this week's links that are worth your time.

Thanks for reading,
Kevin

📖What I'm reading

🩺💻 Health informatics and 🤖 Artificial Intelligence - The GPT-x Revolution in Medicine

Dr Eric Topol's early review of an upcoming book, The AI revolution in Medicine: GPT-4 and beyond. 'So much of what has been written about AI in healthcare deals only with the clinician side, missing the big picture for all the potential benefits for patients. The potential ability for any person to get integrated, high quality, individualized feedback about their own data or queries, is extremely important. It further promotes democratization of medicine and healthcare so long as the output is accurate. The ability for synthetic (derived from office visit conversation) notes to provide education and coaching for the patient, at their particular level of health literacy and cultural background, is striking. Many good examples are presented and I especially liked this prompt and response for hiring GPT-4 as a personal medical consultant.'

🏥 Patient safety - Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

"An EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability."

👨‍💻 Product management - 7 Key Questions for Interviewing a Prospective User of your Product
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1. Capture eyewitness account: Describe the last time you <had X problem>. What happened? What did you do? Why did you do that?

2. Check table stakes: What works for you about <existing solution Y>?

3. Probe for pain: What’s the worst part about trying to <solve problem X?>? How much does this suck?

4. Research cost: How much <money, time, effort> did you spend to <solve problem X with existing solution Y>?

5. Determine the bar: What’s the best experience you’ve ever had in <solving problem X>?

6. Gather potential visions: If you had a magic wand for <problem X>, what would you use it on?

7. Test your thesis: If I gave you a <proposed solution>, what parts about <problem X> would it help with or not help with?"

🤖 Artificial Intelligence - ChatGPT is about to revolutionize the economy. We need to decide what that looks like.
"But Autor also sees a more positive possible outcome: generative AI could help a wide swath of people gain the skills to compete with those who have more education and expertise."
"Two MIT economics graduate students, Shakked Noy and Whitney Zhang, ran an experiment involving hundreds of college-educated professionals working in areas like marketing and HR; they asked half to use ChatGPT in their daily tasks and the others not to. ChatGPT raised overall productivity (not too surprisingly), but here’s the really interesting result: the AI tool helped the least skilled and accomplished workers the most, decreasing the performance gap between employees. In other words, the poor writers got much better; the good writers simply got a little faster."