Predicting Low Information Laboratory Diagnostic Tests

Escalating healthcare costs and inconsistent quality is exacerbated by clinical practice variability. Diagnostic testing is the highest volume medical activity, but human intuition is typically unreliable for quantitative inferences on diagnostic performance characteristics. Electronic medical records from a tertiary academic hospital (2008-2014) allow us to systematically predict laboratory pre-test probabilities of being normal under different conditions. We find that low yield laboratory tests are common (e.g., ~90% of blood cultures are normal). Clinical decision support could triage cases based on available data, such as consecutive use (e.g., lactate, potassium, and troponin are >90% normal given two previously normal results) or more complex patterns assimilated through common machine learning methods (nearly 100% precision for the top 1% of several example labs)