Quoting Chengpeng Mou
📰 Simon Willison's Blog
Analyze healthcare conversations on ChatGPT to identify trends and areas of need, such as hospital deserts and after-hours queries
Action Steps
- Analyze anonymized ChatGPT data to identify healthcare-related conversations
- Identify geographic areas with limited healthcare access, such as hospital deserts
- Determine the timing of healthcare queries to understand when people are seeking information
- Compare the volume of healthcare queries during clinic hours vs. outside clinic hours
- Visualize the data to highlight trends and areas of need
Who Needs to Know This
Data scientists and healthcare professionals can benefit from this information to inform their strategies and improve healthcare accessibility
Key Insight
💡 People are seeking healthcare information outside of traditional clinic hours, highlighting a need for more accessible healthcare services
Share This
💡 2M weekly messages on health insurance & 600K from hospital deserts on ChatGPT!
Full Article
From anonymized U.S. ChatGPT data, we are seeing: ~2M weekly messages on health insurance ~600K weekly messages [classified as healthcare] from people living in “hospital deserts” (30 min drive to nearest hospital) 7 out of 10 msgs happen outside clinic hours — Chengpeng Mou</
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