Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
This work addresses the challenge of determining disease onset from censored data for healthcare analytics, but it is incremental as it builds on existing renewal process and change-point detection methods.
The researchers tackled the problem of inferring chronic treatment onset from incomplete electronic health records by modeling prescription dynamics as a renewal process, showing that their approach yields more temporally plausible onset estimates than naive methods, with performance varying by disease and prescription density.
Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient prescriptions form renewal-based trajectories that provide a continuous signal of disease management. We propose a probabilistic framework to infer chronic treatment onset by modeling prescription dynamics as a renewal process and detecting transitions from sporadic to sustained therapy via change-point detection between a baseline Poisson (sporadic prescribing) regime and a regime-specific Weibull (sustained therapy) renewal model. Using a nationwide ePrescription dataset of 2.4 million individuals, we show that the approach yields more temporally plausible onset estimates than naive rule-based triggering, substantially reducing implausible early detections under strong left censoring. Detection performance varies across diseases and is strongly associated with prescription density, highlighting both the strengths and limits of treatment-based onset inference.