Solving Adherence: How Data-Driven Risk Profiling Can Proactively Increase Your Brand's Ability to Reach Target Patients
The impact of non-adherence to medication is well documented and largely undisputed. Multiple studies have shown that patients' failure to remain committed to their prescribed course of therapy accounts for billions of dollars in additional healthcare spending every year. A large portion of the U.S. population uses co-pay savings programs. These programs generate a vast amount of data, giving manufacturers a unique opportunity to not just reach patients, but also to understand their behavior. By utilizing this data to infer specific reasons for non-adherence and the ideal timing, content, and channel for intervention, manufacturers can create successful patient-engagement strategies that help drive better compliance with therapy.
THE LIMITATIONS OF TRADITIONAL APPROACHES
Most adherence programs involve broad messaging, through a single channel, on a specified cadence. At their best, these communications may be timed with a patient's enrollment and fill cycle. There is a particular model, however, that is largely unused and represents a missed opportunity: Medication-adherence screening questions, asked during program enrollment, can help identify patients most at risk for poor adherence. This level of analysis makes it possible to segment patients into various risk profiles. Patients that are most at risk get the highest touch interventions, while the lowest-risk patients only get the level of engagement they need. These profiles can be refined through predictive modeling on an ongoing basis as additional data from patients' behavior within the co-pay program is collected. This method enables the engagement with the patient to be customized in three key ways: through message timing, through content, and through delivery channel.
Further, traditional adherence-messaging solutions tend to only address some of the factors that lead to poor patient compliance. Most adherence messages address the cost barrier or serve as simple reminders, as forgetfulness and price are the most commonly stated reasons patients give for not remaining on therapy. Factors like concern over side effects, feelings of indifference, lack of a support system, and other more complex barriers go largely unaddressed by these methods. By implementing a risk-modeling strategy that uses co-pay program data, manufacturers can address issues beyond just the base-level factors negatively impacting adherence.
HOW TO MAXIMIZE YOUR ADHERENCE SOLUTION
Improving patient adherence is a winning proposition for all stakeholders in the healthcare system. Adherence strategies achieve optimal impact when they are integrated with a co-pay savings program. Manufacturers seeking to increase the effectiveness of their adherence solution, or develop one if they currently do not have one in place, should follow some core steps to maximize the benefit derived from it. The program enrollment process should include adherence risk-screening questions.
Patients should then be segmented based on the results of this initial analysis, with the segmentation being refined on an ongoing basis as further data is gathered. Adherence-messaging streams should contain multiple forms of content to resonate with patients based on their individual adherence-risk factors and should be delivered via the patient's preferred communication platform as indicated by his or her initial survey response. Messages should also be triggered by patient behaviors, or lack thereof, as demonstrated by data in order to reach patients at times when the opportunity for impact is greatest.
Because traditional approaches to adherence messaging often fail to engage and connect with patients in a personal fashion, they often prove to be marginal at best in driving increased adherence. By adding the small, yet critical, element of patient risk modeling and subsequently tailoring adherence-message content to address various patient profiles, manufacturers can greatly maximize the impact of their programs and deliver a higher level of value for all stakeholders than might otherwise prove feasible.