Streamline’s Approach to Patient No Shows

Track. Predict. Mitigate.

Track Patient No Shows

The first step to solving a problem is to track it. Patient no shows - one of health centers’ biggest problems - is no different. Yet, many health centers don’t have real-time access to patient no show data.

Streamline establishes a baseline for your no show rate and allows you to understand how it changes over time by patient, provider, specialty, and site.

Which specialties have high no show rates? Which providers’ patients are showing up less frequently than in the past? Streamline answers these questions.

Predict Patient No Shows

Many health centers and providers know from personal experience which patients don’t typically show up, but this information isn’t institutionalized nor immune from human error.

Streamline’s data science model predicts patient no shows.

Mitigate Patient No Shows

Many health centers have taken steps to decrease their no show rate - engaged consultants, onboarded tools to send reminder messages, and arranged transportation - but no shows remain a problem.

Streamline predicts patient no shows to identify, in advance, specific patients in a provider's schedule at-risk of no showing, which enables two novel strategies:

  • 1) Targeted outreach

    Knowing which specific patients in a provider's schedule to conduct targeted outreach for - and knowing which not to - reduces no show rates and saves time and money

  • 2) Strategic overbooking

    Predicting no shows allows centers to strategically overbook through identifying where clusters of no shows are likely to occur in a provider’s schedule, which frees them from guesswork, protects providers' well-being, and drives additional revenue

Predicting No Shows:
How Does it Work?

Streamline’s predictive no show model and overbooking method was developed by former Johns Hopkins Public Health data scientists using a comprehensive set of variables that prioritizes patient and provider well-being

Weather closely correlates to no shows… right?

Surprisingly, the variables many expect to closely correlate with no shows - weather, time of day, day of week - are actually not the most important to predicting no shows.

The variables that matter… are pretty simple

The most important variables are lead time (how long ago was the appointment booked?) and a patient’s track record (have they historically shown up?).

90% accurate and getting better

Today, Streamline’s predictive model is 90% accurate.

And what does 90% mean?

If the aggregated value of all predicted no show rates within an hour is above a Streamline-set threshold, then it is 90% likely that a patient overbooked in that hour will be seen conflict-free.

Let’s chat so you can start predicting patient no shows in just a few weeks