Summary
Background: Patient no-shows in outpatient delivery systems remain problematic. The negative
impacts include underutilized medical resources, increased healthcare costs, decreased
access to care, and reduced clinic efficiency and provider productivity.
Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve
overbooking approaches in outpatient settings to reduce the negative impact of no-shows.
Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic
health record system from a single general pediatrics clinic, consisting of 7,988
distinct patients and 104,799 visits along with variables regarding appointment characteristics,
patient demographics, and insurance information. Descriptive statistics were used
to explore the impact of variables on show or no-show status. Logistic regression
was used to develop a no-show predictive model, which was then used to construct an
algorithm to determine the no-show threshold that calculates a predicted show/no-show
status. This approach aims to overbook an appointment where a scheduled patient is
predicted to be a no-show. The approach was compared with two commonly-used overbooking
approaches to demonstrate the effectiveness in terms of patient wait time, physician
idle time, overtime and total cost.
Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold
being 0.74. This threshold successfully predicts the validation dataset with an error
rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction
of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared
to other common flat-overbooking methods.
Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting
for the prediction of an individual patient’s show/no-show status. The predictive
no-show model leads to a dynamic overbooking policy that could improve patient waiting,
overtime, and total costs in a clinic day while maintaining a full scheduling capacity.
Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple
data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860
http://dx.doi.org/10.4338/ACI-2014-04-RA-0026
Keywords
No-shows - overbooking - appointment scheduling - predictive models
