Sub-typing a patient's LDL particles for their size and density, for example, can lead to prescribing the right statin on the first try – ideally reducing the risk of a heart attack. With expensive oncology treatments, stakes may be even higher. Consider the breast cancer patient whose doctor is ordering chemotherapy. A $300 test to determine the levels of HER-2 protein in a breast tumor can rule out the use of a $40,000 chemotherapy regimen that will not work on that patient.
Another example is a test that suggests how quickly a patient will metabolize the drug Coumadin, prescribed to prevent blood clot formation. Determining the optimal dose of Coumadin early on can save multiple tests, office visits, and adverse events.
While precision medicine can help rule out unnecessary, expensive treatments, the tests themselves can be ordered incorrectly. In just a few years, molecular diagnostics has become a $6.2 billion industry that is growing at 21% annually. As with any high-growth medical cost, health plans should stay on top of molecular diagnostic utilization to ensure its appropriateness. However, plans are often unable to measure their own spend on advanced diagnostics, due to today's limited coding scheme.
Just a couple dozen codes now exist, primarily for test methods rather than the specific tests themselves. These codes are applied to more than 2,000 different genetic tests. This creates great confusion and, if plans cannot measure utilization, then how can they effectively manage it?