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The American Journal of Managed Care December 2015
Interest in Mental Health Care Among Patients Making eVisits
Steven M. Albert, PhD; Yll Agimi, PhD; and G. Daniel Martich, MD
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Ilana Graetz, PhD; Jie Huang, PhD; Richard Brand, PhD; Stephen M. Shortell, PhD, MPH, MBA; Thomas G. Rundall, PhD; Jim Bellows, PhD; John Hsu, MD, MBA, MSCE; Marc Jaffe, MD; and Mary E. Reed, DrPH
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Douglas E. Levy, PhD; Vidit N. Munshi, MA; Jeffrey M. Ashburner, PhD, MPH; Adrian H. Zai, MD, PhD, MPH; Richard W. Grant, MD, MPH; and Steven J. Atlas, MD, MPH
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Dennis P. Scanlon, PhD, Associate Editor, The American Journal of Managed Care
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Jeffrey S. McCullough, PhD, Assistant Professor, University of Minnesota School of Public Health; Guest Editor-in-Chief for the health IT issue of The American Journal of Managed Care
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Preventing Patient Absenteeism: Validation of a Predictive Overbooking Model
Mark Reid, PhD; Samuel Cohen, MD; Hank Wang, MD, MSHS; Aung Kaung, MD; Anish Patel, MD; Vartan Tashjian, BS; Demetrius L. Williams, Jr, MPA; Bibiana Martinez, MPH; and Brennan M.R. Spiegel, MD, MSHS
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Preventing Patient Absenteeism: Validation of a Predictive Overbooking Model

Mark Reid, PhD; Samuel Cohen, MD; Hank Wang, MD, MSHS; Aung Kaung, MD; Anish Patel, MD; Vartan Tashjian, BS; Demetrius L. Williams, Jr, MPA; Bibiana Martinez, MPH; and Brennan M.R. Spiegel, MD, MSHS
Electronic health record data can be used to predict patient absenteeism accurately. Predictive overbooking of missed appointments can significantly increase service utilization.
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 overbooking in a gastrointestinal endoscopy clinic—an exemplar resource-intensive environment with a high no-show rate.

Study Design: We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic.

Methods: We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical “1 patient, 1 slot” scheduling.

Results: Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = –5.67; 95% CI, –6.48 to –4.87; P <.0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows.

Conclusions: Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.

Am J Manag Care. 2015;21(12):902-910
Take-Away Points
Absenteeism for scheduled clinical procedures and visits is common and costly. We developed a predictive overbooking system that uses patient- and clinic-level electronic health record data to project future no-shows and cancellations with a high degree of accuracy. We made projected open appointments available to patients willing to be seen promptly.
  • Previous absenteeism, comorbid disease burden, and current mental illness accurately predict no-shows and cancellations.
  • Predictive overbooking could improve service utilization rates from 62% to 97%, allowing dozens of additional patients to be seen weekly.
  • Clinic capacity could be maximized on most days, with minimal and manageable clinic overflow.
Absenteeism for scheduled outpatient visits and procedures—also called “no-show”—occurs frequently in healthcare systems worldwide, resulting in treatment delays, poor use of clinic resources, and significant financial loss.1-13 No-show rates at outpatient clinics range from 12% to 80%, resulting in revenue losses exceeding 20%.13 Patient no-shows diminish clinical productivity, increase appointment lead times for others in the queue, lower patient satisfaction, and reduce quality of care.1-12,14,15

There are many approaches to preventing no-shows, including telephone reminders,16,17 home mailings,16,18,19 text messages,20 and patient navigator programs.16,21,22 However, these interventions yield modest and inconsistent improvements in attendance.16-18,22 Levying fines as a deterrent for no-shows achieves better success,7 but financial sanctions are suboptimal because they disproportionately impact patients with fewer resources.

Another approach is to schedule more patients than there are available appointments (ie, “overbook”).4,23-25 This method is used to maximize “perishable-asset” utilization in the travel and lodging industries by overbooking at a fixed, average historical no-show rate. However, fixed overbooking in healthcare settings can still overburden staff, increase patient wait times, lower patient satisfaction, and potentially increase no-show rates thereafter.8,22 Moreover, it is not acceptable to deny a scheduled service that directly impacts health or survival.

Because fixed overbooking is unlikely to meet the dynamic needs of healthcare settings, an optimal solution should account for each patient’s individualized risk of absenteeism—not just the average clinic no-show rate. Studies that have examined individual predictors of clinic no-shows reveal that patients who have missed appointments previously tend to be uninsured, unmarried, and younger; have active mental health comorbidities such as depression or substance abuse; have poor access to transportation; or have other socioeconomic problems.9,10,12,26-33
Appointment lead-time, urgency of the appointment, timing of the appointment, and clinic proximity also are associated with patient absenteeism.

These patient-level characteristics have been used in some studies to evaluate the impact of predictive (rather than fixed) overbooking in healthcare.14,24,25 Probabilistic computer simulations reveal that predictive overbooking may not only improve patient throughput and reduce staff idle time, but it may also increase wait times and staff overtime on days overbooking exceeded capacity.14,25 Notably, these models have not been tested in a real clinical setting. In this study, we developed a predictive model for no-shows at a Veterans Affairs (VA) healthcare network clinic and validated the model prospectively. This work is topical given recent concerns about scheduling in the VA healthcare system.34 As opposed to wait-listing patients, we sought to develop a means of seeing more patients more quickly. We also desired a model that would utilize electronic health records (EHRs), minimizing impact on patients and allowing for easy implementation across a variety of clinical settings.

We hypothesized that EHRs—a high-volume, highly varied, rapidly delivered “big data” resource—would allow us to employ diverse data points to project open appointments accurately in real time and to offer those spots to additional patients. We tested this approach in a gastrointestinal (GI) endoscopy clinic because it is a model highthroughput, resource-intense environment commonly affected by patient no-shows.5,6,19,33,35-39 We surmised that if the predictive overbooking approach could work in a GI endoscopy clinic, then it might work in other clinical environments also affected by poor service utilization rates, with minimal clinic overflow.

Study Overview
For phase 1 of this study, we used patient- and clinic-level data obtained retrospectively over an 8-month period to develop a predictive model for patient absenteeism. During phase 2, we validated the model using patient data obtained in real time over a 4-month period, testing how well the model projected openings in the clinic schedule and evaluating how using the model to direct scheduling would affect service utilization and patient throughput.

All patients in this study were US military veterans scheduled for all types of outpatient endoscopies (primarily esophagogastroduodenoscopy and colonoscopy) in the VA Greater Los Angeles Healthcare System, a geographically and demographically diverse network of 15 clinics serving 1.4 million veterans. In phase 1, we collected data on 1392 patients scheduled for GI procedures between November 2012 and June 2013; in phase 2, we collected data on an additional 1197 patients scheduled between July and October 2013 (see Table 1 for demographic details). All data were collected in a VA-approved database and obtained through automated searches of the VA EHR. Study design and procedures were formally reviewed and approved by the VA Institutional Review Board (VA IRB # CC 2013-040489). Because this study involved passive data collection, participants were not compensated and did not provide informed consent.

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