First up is a small study on how physicians don’t do very well in interpreting test results. “Medicine’s Uncomfortable Relationship With Math Calculating Positive Predictive Value“:
To make use of these skills, clinicians need access to accurate sensitivity and specificity measures for ordered tests. In addition, we support the use of software integrated into the electronic ordering system that can prevent common errors and point-of-care resources like smartphones that can aid in calculation and test interpretation. The increasing diversity of diagnostic options promises to empower physicians to improve care if medical education can deliver the statistical skills needed to accurately incorporate these options into clinical care.
Or, you could watch this episode on test characteristics:
Then there’s an editorial on the study. “Ensuring Correct Interpretation of Diagnostic Test Results“:
In the meantime, before ordering any test, we must ask ourselves if it is even necessary. Assuming there are efficacious treatments for the disease being tested, what are our thresholds for “ruling out” disease on the low end and “ruling in” disease on the high end of probability, and then, what is the pretest probability of the disease? If your pretest probability falls between those thresholds, is the test accurate enough that a positive or negative test finding will result in a posttest probability that crosses these thresholds? If the test result is not going to change your clinical management, there is no reason for the patient to undergo testing in the first place.
Or, you could watch this episode on Bayes’ Theorem:
I swear, we didn’t plan this. It’s just a testament to how timely Healthcare triage is, I guess. 🙂
