Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
arXiv:2602.23824v1 Announce Type: new 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 wit
arXiv:2602.23824v1 Announce Type: new 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.
Executive Summary
This article proposes a probabilistic framework to infer chronic treatment onset from ePrescription data using a renewal process approach. The authors model prescription dynamics as a renewal process and detect transitions from sporadic to sustained therapy, yielding more temporally plausible onset estimates than naive rule-based triggering. The approach is evaluated using a nationwide ePrescription dataset of 2.4 million individuals, demonstrating its effectiveness in reducing implausible early detections under strong left censoring.
Key Points
- ▸ Probabilistic framework for inferring chronic treatment onset from ePrescription data
- ▸ Renewal process approach to model prescription dynamics
- ▸ Change-point detection to identify transitions from sporadic to sustained therapy
Merits
Improved Temporal Plausibility
The proposed approach yields more temporally plausible onset estimates than naive rule-based triggering, reducing implausible early detections under strong left censoring.
Demerits
Variability in Detection Performance
Detection performance varies across diseases and is strongly associated with prescription density, highlighting the limits of treatment-based onset inference.
Expert Commentary
The proposed renewal process approach offers a significant improvement over traditional methods for inferring chronic treatment onset from ePrescription data. By modeling prescription dynamics as a renewal process and detecting transitions from sporadic to sustained therapy, the authors provide a more nuanced understanding of disease management. However, the variability in detection performance across diseases and prescription density highlights the need for further refinement and consideration of disease-specific factors.
Recommendations
- ✓ Further evaluation of the approach across diverse disease populations and healthcare settings
- ✓ Integration with other data sources, such as EHRs and claims data, to enhance the accuracy and comprehensiveness of treatment onset estimates