HSR&D Citation Abstract
Search | Search by Center | Search by Source | Keywords in Title
Preventing Endoscopy Clinic No-Shows: Prospective Validation of a Predictive Overbooking Model.
Reid MW, May FP, Martinez B, Cohen S, Wang H, Williams DL, Spiegel BM. Preventing Endoscopy Clinic No-Shows: Prospective Validation of a Predictive Overbooking Model. The American journal of gastroenterology. 2016 Sep 1; 111(9):1267-73.
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.
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.
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).
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.