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Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health
Rachel M Goffman1, Shannon L Harris2, Jerrold H May3
1Veterans Engineering Resource Center, VA Pittsburgh Healthcare System, 1010 Delafield Road 001VERC-A, Pittsburgh, PA 15215.
Military Medicine
|November 1, 2017
Summary
A new predictive model accurately identifies patients likely to miss appointments. This allows targeted interventions, significantly reducing no-show rates and improving healthcare efficiency.
Area of Science:
- Health Services Research
- Predictive Modeling
- Healthcare Management
Background:
- Missed healthcare appointments decrease system efficiency and patient access.
- Identifying at-risk patients enables targeted interventions to reduce no-shows.
Purpose of the Study:
- To develop and validate a predictive model for identifying patients with a high probability of missing outpatient appointments.
- To test an intervention strategy for high-risk patients identified by the model.
Main Methods:
- Utilized demographic data, appointment characteristics, and attendance history from four Veterans Affairs facilities.
- Modeled past attendance using an empirical Markov model and developed 24 predictive models via logistic regression.
- Implemented a pilot study with 1,754 high-risk patients, applying live reminder calls 24-72 hours before appointments.
Main Results:
- Past attendance behavior, appointment age, and multiple scheduled appointments were key predictors of no-show probability.
- The intervention reduced the no-show rate in the pilot group from an expected 35% to 12.16% (p < 0.0001).
Conclusions:
- The predictive model effectively identified patients at higher risk of missing appointments.
- Implementing this model allows clinics to focus intensive interventions on high-risk individuals, improving care access and efficiency.
