Health Services Research
MO 18 - HSR 2- Patient Centered Health Services Research
1221 - A Predictive Model for Radiation Oncology No-Show Appointments
Wednesday, September 18
8:20 AM - 8:25 AM
Location: Room W178
Laura Ashack, MD
Department of Radiation Oncology, Northwestern University Feinberg School of Medicine
A Predictive Model for Radiation Oncology No-Show Appointments
L. Ashack1, T. F. Byrd2, K. Jackson3, and S. D. Sanford4; 1Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, 2Department of Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 3Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 4Department of Psychiatry and Behavioral Sciences and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
Purpose/Objective(s): Patients undergoing radiotherapy are vulnerable to no-show visits due to the nature of the treatments being daily, Monday through Friday, over multiple weeks. Additionally, each fraction is essential to achieving optimal oncologic control. The following is a retrospective analysis aimed at determining the predictive factors of radiation oncology (RO) noncompliance within a large, academic medical center. This data will enable early identification of patients at risk for missed appointments.
Materials/Methods: We retrospectively reviewed encounters of patients treated in the RO department from January 2016 – December 2018. Demographic, clinical, and treatment-related factors were collected. Patients from Illinois, Indiana, Wisconsin, and Michigan were included. Significant effects were defined as a p-value of < 0.0060241 (Bonferroni correction) to decrease the false negative rate. Odds ratios (OR) and 95% confidence intervals (CI) were derived using the effect estimates from the logistic regression model.
Results: A total of 55,798 encounters were identified and 39,271 included in the analysis. The overall no-show rate was 6.1%. Patient factors associated with no-show visits included African American race (OR 1.47, CI 1.28-1.69), Medicaid insurance (OR 1.57, CI 1.27-1.95), and tobacco use (OR 1.69, 95% CI 1.41-2.02). Each non-RO no-show and emergency department visit in the prior 3 months increased the odds of a future no-show (OR 1.08, 95% CI 1.05-1.11 & OR 1.14, 95% CI 1.09-1.19, respectively). This was also true for each RO no-show in the prior 2 years (OR 1.08, 95% CI 1.06-1.09). Encounters for treatment had a higher odds of a completed appointed, versus simulation or clinic visits (OR 0.36, 95% CI 0.31 – 0.42). Compared to an appointment from 6-8AM, an appointment from 12-2PM increased the odds of a no-show by 50.5% (OR 1.50, CI 1.26-1.79), and between 3-5PM by 83.6% (OR 1.84, CI 1.53-2.20). The earlier an appointment was scheduled in advance, the greater the odds of a no-show (OR 1.007, CI 1.006-1.008). Median income and distance from the clinic were not associated with no-shows. The model had a predictive accuracy of 94.1%, a positive predictive value of 94.3% for completed appointments, and a 53% negative predictive value for no-shows.
Conclusion: Clinical studies have shown in certain disease sites that missing 2 radiation fractions is associated with worse survival endpoints. These findings highlight the need to develop innovative methods to reduce noncompliance. We aim to use our findings to inform quality improvement initiatives targeting early identification of patients at risk for no-shows, allowing for more equitable and effective resource allocation with hopes of reducing missed appointments.
Author Disclosure: L. Ashack: None. K. Jackson: None. S.D. Sanford: Employee; Abbvie. Consultant; Anne & Robert H. Lurie Children's Hospital. Stock; Abbvie. Patent/License Fees/Copyright; Abbvie.