Using no-show modeling to improve clinic performance

Joanne Daggy, Mark Lawley, Deanna Willis, Debra Thayer, Christopher Suelzer, Po Ching Delaurentis, Ayten Turkcan, Santanu Chakraborty, Laura Sands

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

'No-shows' or missed appointments result in under-utilized clinic capacity. We develop a logistic regression model using electronic medical records to estimate patients' no-show probabilities and illustrate the use of the estimates in creating clinic schedules that maximize clinic capacity utilization while maintaining small patient waiting times and clinic overtime costs. This study used information on scheduled outpatient appointments collected over a three-year period at a Veterans Affairs medical center. The call-in process for 400 clinic days was simulated and for each day two schedules were created: the traditional method that assigned one patient per appointment slot, and the proposed method that scheduled patients according to their no-show probability to balance patient waiting, overtime and revenue. Combining patient no-show models with advanced scheduling methods would allow more patients to be seen a day while improving clinic efficiency. Clinics should consider the benefits of implementing scheduling software that includes these methods relative to the cost of no-shows.

Original languageEnglish (US)
Pages (from-to)246-259
Number of pages14
JournalHealth Informatics Journal
Volume16
Issue number4
DOIs
StatePublished - Dec 2010

Keywords

  • missed appointments
  • no-shows
  • patient scheduling
  • predictive models

ASJC Scopus subject areas

  • Health Informatics

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