In clinical ML where labels arrive weeks later, what is your go-to method for early drift detection without labels? What one statistic, canary cohort, or weak supervision signal saved you from a silent failure, and can you give one brief example?

Question 1: In applications where you might have weeks between receiving the signals for performance from the Model and knowing how the performance will impact its use, waiting for the model's accuracy metrics to decline to identify that the model is failing is unrealistic. Instead, I monitor for covariate shift by continually contrasting the incoming data of feature(s) vs. their training baseline. If the data coming into the Model are significantly different than the data that were used to train the Model, the Model is effectively guessing at the label, even if it is not producing an error. Question 2: The calculation of the Population Stability Index (PSI) on the high-value features is one metric that consistently saves us from having to deal with a model that has silently failed. The cutoff we look for is when the PSI exceeds 0.2; this means there has been a statistically significant shift in the target population. It provides an "early warning" system to the engineering group to inform them that the Model's environment has undergone a change, even before the Clinical Outcomes have been captured. Question 3: We had one instance where a diagnostic tool was quietly updated, resulting in an alteration in how a particular lab value was presented, as the measurement unit had been changed. Due to the active monitoring of Feature Distributions, the PSI alerted us to the change within hours. Had we not received the Signal from the PSI, we would have provided clinicians with inaccurate risk scores for three weeks prior to having the actual Patient Outcomes demonstrate decreased precision. A silent failure in a clinical setting is significantly more impactful than a failure in a System (Crash). The only means of maintaining Trust in our Systems is through embedding Statistical Protection within our Data Pipelines, given that Ground Truth is always delayed (several weeks).

From a business and risk perspective, the most reliable method is monitoring input distribution shifts instead of waiting for outcomes. I've seen teams track feature stability using simple population statistics and alert thresholds. A small canary cohort helped surface drift early. In one case, volume patterns changed quietly before labels arrived. Catching it early prevented compounding errors. The lesson is watch behavior signals. When outcomes lag, inputs tell the story first.