Sep 29

QP 03 - HSR 1: Quick Pitch: From Data to Delivery: Health Services Insights in Radiation Oncology

1014 - Predictors of Bad Debt in Patients Undergoing Radiotherapy

03:15pm - 03:20pm PT

Room 159

Presenter(s)

Laila Gharzai, MD - Northwestern University, Chicago, IL

L. A. Gharzai¹, Y. Liu², Z. Sun³, M. Mullen², G. Sadigh⁴, J. Pottow⁵, R. Jagsi⁶, and L. J. Mady⁷; ¹Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, ²Northwestern University, Chicago, IL, ³Robert H. Lurie Comprehensive Cancer Center, Division of Biostatistics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, ⁴University of California Irvine, Irvine, CA, ⁵University of Michigan, Ann Arbor, MI, ⁶Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, ⁷Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins University, Baltimore, MD

Purpose/Objective(s): Patients undergoing radiotherapy (RT) often face substantial costs associated with treatment. Such costs put patients at risk of going into debt, which is negatively associated with survival. Bad debt, or unpaid bills sent to collections, is a proxy for severe financial distress. We hypothesized that patient-level predictors could identify patients who were at risk of severe financial distress after RT.

Materials/Methods: We identified patients who underwent a course of RT in fiscal year 2023-2024, across seven affiliate sites. Monthly charges were collapsed at the patient level, and billed out of pocket cost (OOPC) was reported. Bad debt was defined as bills for OOPC sent to collections. Multivariable analyses (MVA) were performed to assess predictors of bad debt and paying higher OOPC (paying >mean). To identify an OOPC threshold predicting for medical debt, we dichotomized non-zero OOPC at percentiles (10th–90th) and fit logistic models, identifying the threshold with the highest area under the receiver operating characteristic curve (AUC).

Results: We identified 14,762 patients with a mean age of 64 years (SD 14.7), 55.6% of whom were female (n=8,203), 6.3% were Hispanic (n=932), and half were never smokers (55.3%, n=7861). Most were married/partnered (62.6%, n=9,189) and had government insurance (57.4%, n=8,479). Most patients paid OOPC (61.1%, n=8701), with a mean of $825.80 (SD 2114.5) and median $25 (IQR 0-685.90). A minority of patients had bad debt (2.7%, n=397), with a mean of $1109.90 outstanding (SD 4215.5) after a mean of 9.3 months (SD 3.9). On MVA predicting for bad debt, older patients (OR 0.99 [0.98-1.00], p=0.004) were less likely to have medical debt. Patients who were single (OR 1.43 [95% CI 1.15-1.77], p=0.001), Hispanic (OR 1.65 [1.10-2.40], p=0.013), current smokers (OR 2.46 [1.75-3.39], p<0.001), with commercial insurance (OR 1.83 [1.40-2.38], p<0.001; referent government insurance), or with higher OOPC (OR 1.08 per $1000 [1.05-1.11], p<0.001) were more likely to have bad debt from RT. On MVA predicting for higher OOPC, age (OR 1.02 [1.01-1.02], p<0.001) predicted for higher OOPC; non-English primary language (OR 0.71 [0.57-0.89], p=0.003), commercial insurance (OR 0.27 [0.24-0.31], p<0.001; referent government insurance) predicted for lower OOPC. The threshold for OOPC that best identified bad debt was 50%ile (corresponding to OOPC $25), with an AUC of 0.703 (OR 8.44 [6.23-11.73], p<0.001).

Conclusion: In patients undergoing RT, most patients pay OOPC for treatment and a minority of patients incur bad debt. Patients who are single, Hispanic, current smokers or with non-government issued insurance were more likely to incur bad debt. We identify an OOPC threshold ($25) above which patients were over eight times as likely to incur bad debt. Identifying patients at risk of high OOPC and bad debt as a surrogate for severe financial distress may offer means to tailor patient-facing interventions such as financial navigation to those in most need.