INFOGRAPHIC: Predicting Rx Nonadherence
Apr 24, 2013
This infographic reveals how Express Scripts predicts which patients will stop taking their medications, and how specific, tailored interventions can help.
Click here to download.
The U.S. wasted $317.4 billion last year treating unnecessary medical complications that could have been avoided if patients had taken their medications as prescribed. That’s more money than the country spent treating diabetes, heart disease and cancer, combined. It’s true – nonadherence is the costliest health condition we face.
So why don’t patients take their medications? And which specific patients are most likely to become nonadherent?
Did you know that men who see female physicians are less likely to fill their prescriptions?
Or that parents of young children are more likely to skip doses?
Or that adherence is contagious across partners?
From Insights to Solutions
The findings are fascinating, but they’re also actionable. Based on these insights, the Express Scripts Lab recently launched the proprietary adherence solution, ScreenRx SM. Leveraging the power of predictive modeling, the tool identifies patients at highest risk for not following their doctors' orders. Once identified, patients receive personalized interventions to help them stay on their therapy.
ScreenRx considers more than 400 known factors about the patient, the physician, the disease and the prescribed therapy to identify who is most likely to stop taking their medication. The models are up to 94% accurate in predicting nonadherence one year in advance – nearly 9 times more accurate than what patients self-report.
Multifaceted Problem Demands Tailored Interventions
Furthermore, ScreenRx isolates the most likely reason why a particular patient is at-risk and intervenes appropriately. If a patient is likely to become nonadherent due to behavioral factors such as procrastination or forgetfulness (the majority of the instances), the patient may receive daily alerts, 90-day fills or auto-renewals. If a patient is likely to have clinical questions or concerns about the medication, the patient will receive a pharmacist consultation. If high cost is the primary concern, the patient may be contacted about payment assistance programs, lower cost medication alternatives and lower cost pharmacy options such as home delivery.
So what happens when we focus the power of Big Data on one of our country's biggest problems? Earlier detection of nonadherence... tailored interventions before bad habits get established... and ultimately, better patient outcomes and lower healthcare costs.
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