This is part 2 of a 3 part series on how employers can leverage genomics for healthcare cost control and more.
In recent years healthcare costs specific to pharmacy have peaked and appear to be on a slow, yet gradual decent. With benefit plan redesign, out-of-pocket expenses for many costly drug therapies are shifting from employer to consumer. Thus the value of any given pharmaceutical therapy continues to be an imperative—why spend money (either the consumer’s or the employer’s) on medications that don’t work, or worse yet, medications that cause harm?
Medical science has known for decades that medication works differently across individuals. Drug-drug interactions, drug-food interactions, age, disease states and pregnancy can all influence an individual’s drug response. Advancements in genomic research have helped to highlight that some of those differences are due to our genetics—and by mapping those genes, we can determine what medications to use for best outcomes related to certain conditions. The potential for cost savings to employers and consumers is not only related to greater drug efficacy, but also in the reduction of adverse drug reactions (ADRs) and improved safety with medication use.
Just imagine—rather than 3 to 6 months of a therapy that should work (but doesn’t) including additional medical encounters for symptom relief of side effects, an individual could submit a DNA sample (by blood test or cheek swab) and a profile of gene expression would detail out to their physician a list of medications that should have optimal effect for the individual before the therapy is started. This is the future that pharmacogenomics promises us: precision pharmaceutical therapies with superior clinical outcomes and minimal adverse effects.
Just as we do not know the full expression of the human genome, we do not yet have pharmacogenomics applications for all medications—about 80-85% of medications being used in a general working population can be evaluated through the pharmacogenomics process. Drug interactions and metabolic effects on anticoagulant therapy has been well-studied, but these medications are used for conditions that are rather infrequent in the general working population. Depression is significantly more common in adult, working populations with economic impacts on employers in both healthcare costs and employee productivity. Recent publications have demonstrated potential annual cost savings for depression between USD$3,000-$6,000 per patient when pharmacogenomics is utilized.
Many pharmacogenomics vendors operate separate from an insurance carrier—making them accessible today to both self-funded and fully insured employers. These vendors may offer an initial, no charge, population review of one years’ worth of pharmacy claims information to determine if there are enough members in the plan population to support such services. Seizure disorders, behavioral health (specifically anti-depressants, mood stabilizers, and anti-psychotics), and anticoagulant therapies are ready-made opportunities for pharmacogenomics testing. However, the prevalence of conditions that can be influenced by pharmacogenomics may not be sufficient in your population to support the additional costs of genetic testing. Remember, not every condition treated with a medication has a pharmacogenomics application, thus an employer must have clarity outright for the outcome desired when leveraging this technology. If it is strictly for cost savings, you will need to be a larger group with a higher prevalence of the specified conditions. If you are looking to add value to the healthcare you are purchasing, pharmacogenomics may help in aligning individuals to their optimal medication.
In the future, we can anticipate that the costs of pharmacogenomics testing will decrease as its applicability increases and becomes the standard of practice for most chronic conditions. For now, though it may be a precise solution for a few individuals (including some who could potentially develop into large claimants), pharmacogenomics has a limited impact on an overall plan member population.
We’ll keep our eye on this moving target to see where the opportunities develop to improve health outcomes and costs controls.
PART THREE (December 2018): Preventive Precision Medicine—how a population-level approach to genomics could change the future burden of chronic disease.