Office of Technology Transfer – University of Michigan

A predictive model for strategic overbooking in an urban PT clinic

Technology #6656

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Categories
Researchers
Vahid Lotfi
Managed By
Drew Bennett
Associate Director - Software Licensing 734-615-4004
Publications
Improving an outpatient clinic utilization using decision analysis-based scheduling
Socio-Economic Planning Sciences - Volume 48, Issue 2, June 2014, Pages 115–126, 2014

No-show rates for doctors’ appointments vary, but are as high as 30% of appointments in some settings. No-shows cost the health industry an estimated $150 billion annually. Reducing no-show rates is a difficult problem. This technology uses a predictive model to schedule patients in such a way as to maximize clinic usage. Rather than approach the difficult problem of reducing no-show rates directly, this technology uses the likelihood of a patient being a no-show strategically. This is a major advantage, as the technology can be used in conjunction with other methods to reduce no-shows, such as reminder calls.

Using patient data to maximize clinic efficiency

This technology was developed using data from 367patients at the University of Michigan Flint Physical Therapy Clinic. Several models were compared to determine the optimal method for strategic overbooking of schedules. The technology was specifically developed for use in urban clinics, so is applicable to the vast majority of United States patients. This tool will increase both customer satisfaction and clinical efficiency. While several products for clinical scheduling exist, none use a predictive model like this one. This is an exciting new area, and similar efforts are being made at Purdue University and the University of Texas.

Applications

  • Reduction of patient wait time.
  • Increased efficiency of clinics.
  • Thousands of dollars saved, yearly, in clinics due to increased efficiency.

Advantages

  • Specifically designed to face problems unique to urban clinics.
  • Can be used in conjunction with no-show reduction methods, such as automated reminders.