Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach

TL;DR

This paper introduces a probabilistic renewal process model to accurately infer the onset of chronic treatment from ePrescription data, overcoming limitations of incomplete diagnosis records in electronic health records.

Contribution

It presents a novel renewal process framework for inferring disease onset from prescription data, improving temporal accuracy over naive methods.

Findings

More plausible onset estimates than rule-based methods.

Significant reduction in early false detections under censoring.

Performance varies with disease and prescription density.

Abstract

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.