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Activity Number: 132 - SLDS CSpeed 1
Type: Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319092
Title: A Prediction Model Method for Optimizing Appointment Overbooking in Health Care Clinics Using Electronic Health Care Record Data
Author(s): Nathaniel O'Connell* and Joseph Skelton
Companies: Wake Forest School of Medicine and Wake Forest School of Medicine
Keywords: Electronic Health Record; Random Forest; Classification; Prediction ; Machine Learning; Predictive Overbooking

On average, 23% of outpatient clinic visits result in missed appointments (i.e. ‘no-shows’). ‘No-shows’ lead to decreased clinic productivity, lost clinic revenue, increased healthcare costs, and decreased access to healthcare for patients. To offset these costs, clinics will often overbook some scheduled appointments with a 2nd appointment at the same time. This can lead to decreased patient satisfaction, longer wait times, and increased overtime-pay for staff. The strategy of “predictive overbooking” attempts to predict patients with the highest ‘no-show’ risk for whom to overbook in order to maximize clinic efficiency and minimize costs. In this context, we propose a probabilistic approach for optimizing the classification of predicted ‘no-shows’ in prediction models, taking into account the predicted probability of no-shows, scheduling collisions, and costs associated with each. Using electronic health record data from 14 pediatric sub-specialty clinics, we develop a Random Forest model predicting ‘no shows’ for each clinic and demonstrate our method for classification leads to improved clinic utilization and decreased costs compared to other common classification thresholds.

Authors who are presenting talks have a * after their name.

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