🧵 In Summary
Prediction models are not “set and forget.” They must be constantly checked, updated, and re-evaluated—before and after clinical use.
With thoughtful updating, we can keep models useful and safe over time.
📄 Read our commentary: kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F...
🚨 What About After Implementation?
Once a model is used in practice, it changes patient outcomes (e.g., high-risk patients get treated and avoid the outcome).
This shifts the relationship between risk factors and outcomes. Updating without accounting for this is risky.
⚖️ Fairness Under Threat
Temporal drift can impact some groups more than others.
Even worse, updating models doesn’t guarantee fairness. It can reduce—or worsen—performance in minority groups.
We need to monitor algorithmic fairness, not just accuracy.
💻 The Catch? Complexity
Dynamic updates need:
Continuous data
More computing power
Digital infrastructure in place
Also, no method kept the original performance in later years. So, updating helps—but doesn’t fix everything.
In our commentary, we have expanded on the findings of the study, the impacts of temporal drift and the importance of addressing it.