Clinical prediction models are increasingly embedded within healthcare systems, yet their performance deteriorates over time due to temporal drift. Drift arises from changes to population characteristics, data recording practices, or underlying clinical concepts, and can lead to miscalibrated risk estimates that compromise patient safety.

We compare multiple strategies to detect (and repair) temporal drift including the monitoring of performance metrics, analysis of model residuals, and assessment of input data stability. We apply these approaches to a real-world case study using data from Connected Bradford, evaluating a logistic regression model aligned with QRISK‑2 for predicting 10-year risk of heart attack or stroke. Model behaviour was assessed monthly over a seven year period (2008 to 2015), with recalibration triggered whenever predefined thresholds were exceeded.

Our findings show clear evidence of temporal drift, with degradation in calibration and increasing divergence in residual distributions over time. Approaches based on maintaining performance thresholds produced the most accurate and stable predictions, although methods using model residuals offered similar performance. Regular recalibration at fixed intervals demonstrated reasonable accuracy while offering operational advantages due to predictable resource requirements. Methods independent of model residuals, such as discrimination error, detected drift without requiring long term outcome data and may therefore be more viable in contexts with substantial delays before outcomes can be observed.

Overall, the results highlight the importance of systematic drift monitoring for clinical prediction models intended for deployment at scale. The software library released as part of this research provides practical tools for detecting and mitigating drift, with clear trade-offs between statistical performance, regulatory considerations, and real-world feasibility.