Related Experiment Video
Updated: Mar 21, 2026
Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Time dependent patient no-show predictive modelling development
Yu-Li Huang1, David A Hanauer2
1Mayo Clinic Minnesota, Rochester, Minnesota, USA.
Insights
Predictive no-show models using past appointment data significantly improve clinic scheduling efficiency. Incorporating patient history enhances predictability, reducing costs and optimizing patient flow.
Area of Science:
- Healthcare Management
- Operations Research
- Data Science
Background:
- Patient no-shows disrupt clinic operations, leading to increased costs and reduced access to care.
- Existing predictive models often lack the granularity to capture the dynamic nature of patient behavior.
Purpose of the Study:
- To develop evidence-based predictive no-show models.
- To incorporate past appointment status as a time-dependent predictor.
- To enhance the predictability of patient no-shows in a clinical setting.
Main Methods:
- A retrospective analysis of a 10-year dataset from a pediatric clinic (7,291 patients).
- Logistic regression was used to build 26 predictive models based on historical appointment data.
- Simulation was employed to evaluate model effectiveness on operational costs.
Main Results:
- Model predictability (misclassification rate and AUC) improved with increased historical appointment data.
- The optimal overbooking strategy involved up to the 16th predictive model.
- This approach reduced cost per patient by 9.4% and allowed for two additional patients per day.
Conclusions:
- Predictive no-show models incorporating time-dependent patient history offer significant improvements in clinic scheduling.
- Systematic implementation is needed to demonstrate robustness across various scheduling systems.
- Accurate no-show prediction enables proactive scheduling, mitigating the negative impacts of missed appointments.
Abstract:
Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.
More Related Videos
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Related Concept Videos
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
01:27Pharmacodynamic Models: Overview
01:15Drug Concentration Versus Time Correlation
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:29Factors Affecting Protein-Drug Binding: Patient-Related Factors
Age stands as a key determinant in protein-drug binding. Neonates, characterized by low albumin content, experience heightened concentrations of unbound drugs such as phenytoin and...
01:05Noncompartmental Analysis: Mean Residence Time
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...