Some of the nation’s largest pharmacy benefit management companies have made public their intentions to improve adherence by leveraging available patient clinical data to help patients improve adherence to medication therapies for better outcomes and lower overall health care costs. Under this banner, a pharmacist can be empowered to intervene with patients who have stopped taking medications or who take them less frequently than originally prescribed. This personalized information delivered by a trusted health care provider at a time when the patient is in a teachable moment has and will continue to lead to effective interventions.

But how do you intervene on a patient that never makes it to the pharmacy? A study published last week in the Archives of Internal Medicine reported that high copayments deter patients with newly diagnosed chronic conditions from initiating their drug therapy, especially if they have little experience with taking medications.

While the study suggests that lowering co-payments may encourage patients to begin and adhere to drug therapy, I see a big opportunity to inject more value into the physician interaction by arming them with timely, relevant, patient-specific information at the time of prescribing that can minimize the co-payment obstacle and improve adherence.

Using advanced real-time clinical decision support coupled with e-prescribing, when appropriate, a physician can identify safety- and formulary-checked therapeutic alternatives that have a lower co-pay to encourage the patient to start their drug therapy right away. The thought is not to place additional burden on the physician whose day is already taxed with patient visits and administrative work. By building the advanced analytics capability into the e-prescribing application, the work is done for them.

We all know about alert fatigue with e-prescribing and the rate at which alerts are overridden, and what I’m talking about is not an alert. It’s just simply providing the physician with drug options driven by actionable health intelligence derived from patient data all with the intention of improving the likelihood of adherence. This is clinical decision support at its best.

As CMS shapes and enforces its criteria for e-prescribing incentives, it makes sense to give weight to any mechanisms proven to improve adherence to drug therapy.

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