Literature DB >> 21216805

Using no-show modeling to improve clinic performance.

Joanne Daggy1, Mark Lawley, Deanna Willis, Debra Thayer, Christopher Suelzer, Po-Ching DeLaurentis, Ayten Turkcan, Santanu Chakraborty, Laura Sands.   

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.

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Year:  2010        PMID: 21216805     DOI: 10.1177/1460458210380521

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  29 in total

1.  Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

Authors:  Jimmy Chen; Isaac H Goldstein; Wei-Chun Lin; Michael F Chiang; Michelle R Hribar
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

2.  Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence.

Authors:  Daniel M Blumenthal; Gaurav Singal; Shikha S Mangla; Eric A Macklin; Daniel C Chung
Journal:  J Gen Intern Med       Date:  2015-01-14       Impact factor: 5.128

3.  Cancelled Primary Care Appointments: A Prospective Cohort Study of Diabetic Patients.

Authors:  Sara McComb; Zhiyi Tian; Laura Sands; Ayten Turkcan; Lingsong Zhang; Shree Frazier; Mark Lawley
Journal:  J Med Syst       Date:  2017-02-18       Impact factor: 4.460

4.  Factors associated with patient no-show rates in an academic otolaryngology practice.

Authors:  Caitlin E Fiorillo; Allyson L Hughes; Chen I-Chen; Philip M Westgate; Thomas J Gal; Matthew L Bush; Brett T Comer
Journal:  Laryngoscope       Date:  2017-08-16       Impact factor: 3.325

5.  A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*. An Application in Joint Prediction of Appointment Miss-opportunities across Multiple Clinics.

Authors:  Adel Alaeddini; Seung Hee Hong
Journal:  Methods Inf Med       Date:  2017-06-07       Impact factor: 2.176

6.  Patient no-show predictive model development using multiple data sources for an effective overbooking approach.

Authors:  Y Huang; D A Hanauer
Journal:  Appl Clin Inform       Date:  2014-09-24       Impact factor: 2.342

7.  A Probabilistic Patient Scheduling Model with Time Variable Slots.

Authors:  Danae Carreras-García; David Delgado-Gómez; Enrique Baca-García; Antonio Artés-Rodriguez
Journal:  Comput Math Methods Med       Date:  2020-09-01       Impact factor: 2.238

8.  Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial.

Authors:  Sachin J Shah; Patrick Cronin; Clemens S Hong; Andrew S Hwang; Jeffrey M Ashburner; Benjamin I Bearnot; Calvin A Richardson; Blair W Fosburgh; Alexandra B Kimball
Journal:  J Gen Intern Med       Date:  2016-08-08       Impact factor: 5.128

9.  Initial integration of chiropractic services into a provincially funded inner city community health centre: a program description.

Authors:  Steven R Passmore; Audrey Toth; Joel Kanovsky; Gerald Olin
Journal:  J Can Chiropr Assoc       Date:  2015-12

10.  Preventing Endoscopy Clinic No-Shows: Prospective Validation of a Predictive Overbooking Model.

Authors:  Mark W Reid; Folasade P May; Bibiana Martinez; Samuel Cohen; Hank Wang; Demetrius L Williams; Brennan M R Spiegel
Journal:  Am J Gastroenterol       Date:  2016-07-05       Impact factor: 10.864

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