  Objective: Reflex testing protocols allow clinical laboratories to perform
second line diagnostic tests on existing specimens based on the results of
initially ordered tests. Reflex testing can support optimal clinical laboratory
test ordering and diagnosis. In current clinical practice, reflex testing
typically relies on simple "if-then" rules; however, this limits their scope
since most test ordering decisions involve more complexity than a simple rule
will allow. Here, using the analyte ferritin as an example, we propose an
alternative machine learning-based approach to "smart" reflex testing with a
wider scope and greater impact than traditional rule-based approaches. Methods:
Using patient data, we developed a machine learning model to predict whether a
patient getting CBC testing will also have ferritin testing ordered, consider
applications of this model to "smart" reflex testing, and evaluate the model by
comparing its performance to possible rule-based approaches. Results: Our
underlying machine learning models performed moderately well in predicting
ferritin test ordering and demonstrated greater suitability to reflex testing
than rule-based approaches. Using chart review, we demonstrate that our model
may improve ferritin test ordering. Finally, as a secondary goal, we
demonstrate that ferritin test results are missing not at random (MNAR), a
finding with implications for unbiased imputation of missing test results.
Conclusions: Machine learning may provide a foundation for new types of reflex
testing with enhanced benefits for clinical diagnosis and laboratory
utilization management.
