  The COVID-19 pandemic has dramatically changed how healthcare is delivered to
patients, how patients interact with healthcare providers, and how healthcare
information is disseminated to both healthcare providers and patients.
Analytical models that were trained and tested pre-pandemic may no longer be
performing up to expectations, providing unreliable and irrelevant learning
(ML) models given that ML depends on the basic principle that what happened in
the past are likely to repeat in the future. ML faced to two important
degradation principles, concept drift, when the underlying properties and
characteristics of the variables change and data drift, when the data
distributions, probabilities, co-variates, and other variable relationships
change, both of which are prime culprits of model failure. Therefore, detecting
and diagnosing drift in existing models is something that has become an
imperative. And perhaps even more important is a shift in our mindset towards a
conscious recognition that drift is inevitable, and model building must
incorporate intentional resilience, the ability to offset and recover quickly
from failure, and proactive robustness, avoiding failure by developing models
that are less vulnerable to drift and disruption.
