title: Preventing patient absenteeism: validation of a predictive overbooking model.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Reid
authors.forename: Samuel
authors.initials: S
authors.lastname: Cohen
authors.forename: Hank
authors.initials: H
authors.lastname: Wang
authors.forename: Aung
authors.initials: A
authors.lastname: Kaung
authors.forename: Anish
authors.initials: A
authors.lastname: Patel
authors.forename: Vartan
authors.initials: V
authors.lastname: Tashjian
authors.forename: Demetrius L
authors.initials: DL
authors.lastname: Williams
authors.forename: Bibiana
authors.initials: B
authors.lastname: Martinez
authors.affiliation: West Los Angeles VA Medical Center, 11301 Wilshire Blvd, Bldg 115, Rm 215, Los Angeles, CA 90073. E-mail: brennan.spiegel@cshs.org.
authors.forename: Brennan M R
authors.initials: BM
authors.lastname: Spiegel
mesh_terms: D000465:Algorithms; D000554:Ambulatory Care Facilities; D001071:Appointments and Schedules; D015897:Comorbidity; D006801:Humans; D001523:Mental Disorders / Q000150:complications; D015999:Multivariate Analysis; D000067455:No-Show Patients; D012189:Retrospective Studies; D014481:United States; D014493:United States Department of Veterans Affairs