How can you use machine learning to reduce patient no-show rates?

This is a headache for hospitals and clinics. No-shows are lost revenue and overcoming it can be expensive and chaotic – either with additional FTE headcount or by overbooking time slots.

This is a really great use case for machine learning. In this case, it’s a form of Tabular AI, not Generative AI, but still interesting I think.

So how do you do this? What are the most important features in building the model? And how can a hospital or health system embed it into their existing workflow?

You’ll have to watch below in today’s edition of #CodetoCare, my video series on AI & ML, to find out! I’m sharing an in-depth case study in the video.

If you have any specific questions, drop them in the comments. I’m enjoying your input and I might even make a video responding to them.

#AI #artificialintelligence #genai

Check out my LinkedIn:   / donwoodlock  

0:00 - 0:45 - Introduction: Reducing No-Shows with AI in Healthcare
0:45 - 2:00 - Tackling the No-Show Problem with AI Innovations
2:00 - 4:00 - Initial Challenges: Understanding AI in Patient Management
4:00 - 6:30 - Predictive Modeling with AI: Anticipating Patient No-Shows
6:30 - 9:00 - Workflow Integration: Implementing AI Tools in Practice
9:00 - 11:00 - Analyzing the Impact: Results from AI Predictive Models
11:00 - 12:30 - Key Features: What Influences AI Model Accuracy?
12:30 - 13:01 - Conclusion: The Future of AI in Reducing Patient No-Shows

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