Study Suggests Data from Electronic Health Records Can Predict and Possibly Prevent Missed Patient Appointments
Absenteeism for scheduled outpatient visits and procedures, also called "no-show," occurs frequently in healthcare systems worldwide, causing treatment delays, poor utilization of clinic resources, and significant financial loss. No-show rates at outpatient clinics range from 12% to 80%, resulting in revenue losses exceeding 20%. This study sought to develop a model that identifies patients at high risk for missing scheduled appointments (no-shows and cancellations), and to project the impact of predictive over-booking in a gastrointestinal (GI) endoscopy clinic – a resource-intensive environment with a high no-show rate. Investigators hypothesized that electronic health records (EHRs), a "big-data" resource within VA, would allow the use of diverse data points to project open appointments accurately in real-time, and to offer those spots to additional patients. Data were collected for 1,392 Veterans scheduled for GI procedures between 11/12 and 6/13 for the purpose of model development, and then for an additional 1,197 Veterans between 7/13 and 10/13 for real-time testing of the model. Predictor variables analyzed included demographics, clinical diagnoses (e.g., recent history of depression), and patient attendance histories. Outcomes included no-shows and cancellations made too late to rebook.
- Information from electronic health records can accurately predict whether patients will no-show. The model used in this study was able to correctly classify 711 out of 888 attended appointments, and 317 out of 538 missed appointments.
- The strongest predictor of no-show was a patient's cancellation history – the proportion of all outpatient appointments missed. Veterans with histories of mood or substance use disorder, and those with a greater overall disease burden also were less likely to keep appointments.
- Predictors of being more likely to keep appointments included: being married, having a history of diverticular disease, attending a colonoscopy education class, and having care partly funded by VA.
- Urgency of appointment, race, ethnicity, and day of the week of appointment were not significant predictors of appointment no-shows.
- Compared to a strategy that employs a fixed level of overbooking, predictive over-booking was much less likely to lead to days where the clinic was substantially over- or under-booked.
- Predictive over-booking could improve service utilization rates from 62% to 97%, allowing dozens of additional patients to be seen weekly. This would allow for clinic capacity to be maximized on most days, with minimal and manageable clinic overflow.
- The patient population in this study was scheduled for GI appointments in only one VAMC.
- Collection of data was limited to a few months, thus seasonal trends could not be examined.
- In the validation phase, investigators projected what would have occurred had patients been overbooked based on predicted openings but did not actually change scheduling.
This study was funded by HSR&D (IIR 12-055). Drs. Reid and Spiegel are part of HSR&D's Center for the Study of Healthcare Innovation, Implementation and Policy in Los Angeles, CA.
Reid M, Cohen S, Wang H, Kaung A, Patel A, Tashjian V, Williams D, Martinez B, and Spiegel B. Preventing Patient Absenteeism: Validation of a Predictive Overbooking Model. American Journal of Managed Care. December 2015;21(12):902-910.