Addressing wasted healthcare dollars and ineffective prescriptions

Blog 02

Bret S. Stetka, MD

A recent review published in JAMA reported that the annual monetary waste in the U.S. healthcare system is approaching a staggering 1 trillion dollars. Over half of this is due to patients being prescribed drugs that they don’t actually respond to. Much of the spending on medications is also due to expensive specialty treatments, even if drugmaker rebates are taken into account.

At the front of the pricey, over-prescribed list are medications called TNF-inhibitors, which modulate the immune system and help alleviate autoimmune disorders like rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis.

When TNF-inhibitors work, they work. Many patients experience relief from their symptoms and improved quality of life. 

The problem is, especially in the case of RA, these medications aren’t always effective. Data shows that only around one third of patients with RA actually have an adequate response to anti-TNF therapy. Patients often end up debilitated from painful, swollen joints; unable to climb the stairs, or even manage their car keys.

Researchers at Scipher Medicine hope to address the problem of ineffective prescribing and wasted healthcare dollars by better individualizing care for patients with RA. Their PrismRA blood test identifies a panel of genetic variants that predict which patients are likely to respond to TNF inhibitor therapies, and which are not.

“Patients waste valuable time, and the healthcare system wastes resources on ineffective prescription drugs when the patient’s likelihood of response to therapy is unknown before starting treatment,” says Dr. Erin Connolly-Strong, Head of Medical Affairs at Scipher. “Scipher’s technology allows for the development and commercialization of tests to address this challenge…so potential non-responders can access alternative effective drugs from day one.” Scipher plans to expand their technology to a number of other conditions, and, in the near future, use it to identify novel drug targets based on individual genetic profiles.