This is what healthcare machine learning looks like.

Customers succeed with Skafos.

Metis Machine’s team of data scientists and machine learning experts partners with companies who know ML gives them a competitive advantage.

Use Case: Appointment No-Show Predictor

Goal: Predict and Reduce the Risk of Patient No-Shows

Data + Analytics + Expertise = Results

When patients don’t show-up for appointments, there are consequences and expense. In many cases the result is a swirl of rebookings and phone calls, worse can be a frustrated team, in other cases there are health consequences.

Metis Machine has helped healthcare providers predict and reduce the risk of patient no-shows, enabling providers to proactively manage and even avoid schedule gaps and inefficient resource utilization. As a result of highlighted high-risk no shows, active measures to reduce no-shows include:

  • Overlapping bookings
  • Pre-appointment notification
  • Requested patient confirmation

Bottom line, a no-show predictor opens the door to massive savings that can pay for itself in the first month of operation.


Our ML Jump Start services are designed to help you gain quick insights and value from machine learning so your first effort has a lasting impact where it counts–your business. Leverage our expertise and give your team a head start so that cycles are not spent re-learning the mistakes others have already made. If you think that is worth setting up a 15 min call, get in touch.


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