Mark W Reid1,2, Folasade P May1,3, Bibiana Martinez2, Samuel Cohen1, Hank Wang4, Demetrius L Williams1,2, Brennan M R Spiegel1,2,5,6. 1. Department of Gastroenterology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA. 2. Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, California, USA. 3. Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California, USA. 4. Kaiser Permanente Northern California, Oakland, California, USA. 5. Department of Health Policy and Management, UCLA Fielding School of Public Health, UCLA, Los Angeles, California, USA. 6. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Abstract
OBJECTIVES: Patient absenteeism for scheduled visits and procedures ("no-show") occurs frequently in healthcare systems worldwide, resulting in treatment delays and financial loss. To address this problem, we validated a predictive overbooking system that identifies patients at high risk for missing scheduled gastrointestinal endoscopy procedures ("no-shows" and cancellations), and offers their appointments to other patients on short notice. METHODS: We prospectively tested a predictive overbooking system at a Veterans Administration outpatient endoscopy clinic over a 34-week period, alternating between traditional booking and predictive overbooking methods. For the latter, we assigned a no-show risk score to each scheduled patient, utilizing a previously developed logistic regression model built with electronic health record data. To compare booking methods, we measured service utilization-defined as the percentage of daily total clinic capacity occupied by patients-and length of clinic workday. RESULTS: Compared to typical booking, predictive overbooking resulted in nearly all appointment slots being filled-2.5 slots available during control weeks vs. 0.35 slots during intervention weeks, t(161)=4.10, P=0.0001. Service utilization increased from 86% during control weeks to 100% during intervention weeks, allowing 111 additional patients to undergo procedures. Physician and staff overages were more common during intervention weeks, but less than anticipated (workday length of 7.84 h (control) vs. 8.31 h (intervention), t(161)=2.28, P=0.02). CONCLUSIONS: Predictive overbooking may be used to maximize endoscopy scheduling. Future research should focus on adapting the model for use in primary care and specialty clinics.
OBJECTIVES:Patient absenteeism for scheduled visits and procedures ("no-show") occurs frequently in healthcare systems worldwide, resulting in treatment delays and financial loss. To address this problem, we validated a predictive overbooking system that identifies patients at high risk for missing scheduled gastrointestinal endoscopy procedures ("no-shows" and cancellations), and offers their appointments to other patients on short notice. METHODS: We prospectively tested a predictive overbooking system at a Veterans Administration outpatient endoscopy clinic over a 34-week period, alternating between traditional booking and predictive overbooking methods. For the latter, we assigned a no-show risk score to each scheduled patient, utilizing a previously developed logistic regression model built with electronic health record data. To compare booking methods, we measured service utilization-defined as the percentage of daily total clinic capacity occupied by patients-and length of clinic workday. RESULTS: Compared to typical booking, predictive overbooking resulted in nearly all appointment slots being filled-2.5 slots available during control weeks vs. 0.35 slots during intervention weeks, t(161)=4.10, P=0.0001. Service utilization increased from 86% during control weeks to 100% during intervention weeks, allowing 111 additional patients to undergo procedures. Physician and staff overages were more common during intervention weeks, but less than anticipated (workday length of 7.84 h (control) vs. 8.31 h (intervention), t(161)=2.28, P=0.02). CONCLUSIONS: Predictive overbooking may be used to maximize endoscopy scheduling. Future research should focus on adapting the model for use in primary care and specialty clinics.
Authors: Mark W Reid; Samuel Cohen; Hank Wang; Aung Kaung; Anish Patel; Vartan Tashjian; Demetrius L Williams; Bibiana Martinez; Brennan M R Spiegel Journal: Am J Manag Care Date: 2015-12 Impact factor: 2.229
Authors: Folasade P May; Mark W Reid; Samuel Cohen; Francis Dailey; Brennan M R Spiegel Journal: Gastrointest Endosc Date: 2016-09-10 Impact factor: 9.427
Authors: Valerie Gausman; Giulio Quarta; Michelle H Lee; Natalia Chtourmine; Carmelita Ganotisi; Frances Nanton-Gonzalez; Chui Ling Ng; Jungwon Jun; Leslie Perez; Jason A Dominitz; Scott E Sherman; Michael A Poles; Peter S Liang Journal: J Clin Gastroenterol Date: 2020-02 Impact factor: 3.174
Authors: John F Steiner; Chan Zeng; Angela C Comer; Jennifer C Barrow; Jonah N Langer; David A Steffen; Claudia A Steiner Journal: JAMA Netw Open Date: 2021-03-01