Preventing patient absenteeism: validation of a predictive overbooking model

Published in NCBI :
Reid M, Cohen S, Wang H, Kaung A, Patel A, Tashjian V, Williams DL Jr, Martinez B, Spiegel BM1.



To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate.


We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic.


We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling.


Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows.


Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.