Predicting Patient No-shows

1 min read

Patient no-shows are a persistent problem impacting all levels of healthcare, from patient outcomes to revenue. Many studies have been conducted analyzing a variety of factors, including a patient’s personal no-show history, patient demographics, appointment times, day of the week, rurality or provider location, appointment lead time, insurance coverage type, visit type, and medical specialty, in order to try to predict the likelihood a patient will no-show. Here are some of the common indicators:

Factors That Indicate A Higher Probability Of No-show

  • New patient visits
  • Rural patients
  • Summer appointments
  • Insured patients
  • Younger demographics
  • Lead time exceeding two weeks
  • Monday or Friday appointments

While studies consistently yield results like those above, the variability of patient and practice types make it impossible to develop an industry standard. No-show rates can vary greatly, as low as 5% to over 50% in some cases, fluctuating for any practice at any time for no apparent reason. To be of any use, predictability models really need to be created in-house based on the unique circumstances of your practice… but the duration and resources needed to execute them are often too much for practices to manage.

In this case practices may prefer to strategize around their overall rate, or the national average of 18.8%, rather than the individual factors contributing to them. While this is not the most precise approach, it can still reveal patterns in patient behavior and allow for monitoring the success of new strategies. When no-shows can be predicted it is easier to implement targeted strategies to offset their impacts, such as reducing staff or employing unique scheduling strategies.

Want to learn more about combating the impact of patient no-shows? Access our white paper:

The Wild Card: Strategizing Around The Unpredictability And Inevitability Of No-shows