A Bayesian-driven approach to identify racial inequities in longitudinal care delivery

Measures of healthcare quality and equity often overlook when care is delivered, potentially masking important disparities. We present a novel, time-aware approach to detect racial inequities in hospice use among 100,480 Medicare beneficiaries with advanced cancer. Tracking hospice initiation over the final 200 days of life, we introduce a daily "difference signal" that shows how utilization patterns change over time. Using a Bayesian framework to quantify uncertainty and applying clinically meaningful thresholds, we pinpoint when disparities arise and how large they are. Compared to a conventional, time-agnostic benchmark, our approach reveals substantial day-level disparities that aggregate measures can miss. Notably, while overall measures often show greater hospice access for white patients, our temporal analysis frequently found earlier hospice use by patients of color, demonstrating how timing can reverse apparent patterns. By identifying when disparities emerge, our method offers more actionable targets for interventions and future digital health equity efforts.