Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration

• Published in: Military medicine Vol. 182; no. 5; pp. e1708 - e1714
• Main Authors: Goffman, Rachel M, Harris, Shannon L, May, Jerrold H, Milicevic, Aleksandra S, Monte, Robert J, Myaskovsky, Larissa, Rodriguez, Keri L, Tjader, Youxu C, Vargas, Dominic L
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-05-2017
• Subjects: Adult; Appointments and Schedules; Big Data; Clinics; Colleges & universities; Data mining; Expected values; Female; Health care access; Health care policy; Hospital administration; Humans; Intervention; Logistic Models; Male; Medicine; Mental health; Middle Aged; No-Show Patients - economics; No-Show Patients - statistics & numerical data; Outpatients - psychology; Outpatients - statistics & numerical data; Patient Compliance - psychology; Patient Compliance - statistics & numerical data; Patients; Pilot Projects; Primary care; Risk Assessment - methods; Risk Assessment - standards; Scheduling; United States; United States Department of Veterans Affairs - organization & administration; United States Department of Veterans Affairs - statistics & numerical data; Variables; Veterans; Veterans - psychology; Veterans - statistics & numerical data
• ISSN: 0026-4075; 1930-613X
• DOI: 10.7205/MILMED-D-16-00345

Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows.
Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments.
Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2.
Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001).
The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.