A212 PREDICTIVE OVERBOOKING TO PREVENT ENDOSCOPY CLINIC NONATTENDANCE: MODEL DEVELOPMENT. (15th March 2019)

• Record Type: Journal Article
• Authors: Lu, T; Khan, S I; Hookey, L C
• Is Part Of: Journal of the Canadian Association of Gastroenterology. Volume 2(2019)Supplement 2
• Page Start: 414
• Page End: 415
• Publication Date: 2019-03-15
• Subjects: Gastroenterology -- Periodicals; 616.33005
• DOI: 10.1093/jcag/gwz006.211
• Languages: English
• ISSNs: 2515-2084

Abstract

Abstract: Background: Outpatient endoscopy is a procedure that has a historically high nonattendance rate. This situation runs counter to the current logic of rationalizing healthcare resources in Canada and necessitates the development of novel strategies to improve efficiency in care delivery. Given the success of the predictive overbooking model in the Veterans Affairs Greater LA Healthcare System developed by Reid et al. in 2015, we wondered if a similar model could be implemented in Canada. Aims: This study aims to develop an algorithm based on electronic health record data for identifying patient absenteeism at the outpatient endoscopy clinic at the Kingston Health Sciences Centre - Hotel Dieu Hospital Site in Kingston, Ontario. Methods: In this retrospective case-control study, we manually reviewed 1219 charts from March 2017 and May 2017. Patients included in this study were scheduled for esophagogastroduodenoscopy, flexible sigmoidoscopy, colonoscopy, and other procedures requiring time in the endoscopy suite such as paracenteses. We collected data on previously identified factors that were found to impact outpatient endoscopy attendance rates and then fitted a multivariate logistic regression model to evaluate nonattendance risk. Results: Univariate analyses identified several independently significant variables (p < 0.05) for no show including the season of the appointment date, bowel preparation requirements, marital status, referral type, indication for procedure, previous absenteeism from endoscopic procedures, cancellation proportion, and history of previous GI procedure. Further multivariate analysis identified three statistically significant predictors (p < 0.05) for our model including having a winter appointment date, having a high proportion of cancelled healthcare appointments to total healthcare appointments, and being referred to endoscopy by a specialist physician, including those triaged directly to endoscopy. Conclusions: Electronic health record data can be used to identify predictors of patient absenteeism and to generate a predictive model for nonattendance to outpatient endoscopic procedures. Interestingly, the winter season has a significant impact on no show rates and suggests a specific window of opportunity for predictive overbooking. Next steps include the prospective validation of this model to determine its accuracy in predicting nonattendance. Funding Agencies: None