Patient no-show predictive model development using multiple data sources for an effective overbooking approach

Abstract

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.

Keywords: No-shows; appointment scheduling; overbooking; predictive models.

Fig. 1

The relative ratio to the reference level for all 15 variables included in the logistic regression model

Fig. 2

Threshold probability (p) determination for cost ratios of 2 and 3. The probability is determined to be at the minimum for the weighted sum of Type I and Type II errors.

Fig. 3

The histogram of the predictive no-show rate and the Gamma distribution fit from Goodness of Fit test with Anderson-Darling (AD) statistic, based on the eight years of training data. For the curve above the AD statistic was 0.548 with a p-value of 0.185.

Fig. 4

Comparison of overbooked patient frequencies per day among three overbooking methods for the first 100 runs of the 1600 run simulation. The horizontal dashed line represents a static no-show rate of 4 per day for the Random (R) and Evenly-distributed (E) overbooking approach, based on a 14% historic no-show rate. The variable solid line represents the proposed (P) approach for overbooking based on the specific characteristics of the patients created for each simulation run. This latter approach (P) can more flexibly accommodate the variable likelihood of no-shows each day compared to the standard approaches.