Yet even this effort may largely be wasted because it’s still too general. It doesn’t take into account, for example, all the members who are already following this routine. Receiving a reminder to do something they’re already doing is an annoyance and a distraction that is likely to make them less receptive to future messages from the health payer.
A better approach would be to hyper-target members whose claims and/or medical records indicate they are not filling their prescriptions or taking their medications as prescribed, and deliver meaningful messages to help convince them to change their behavior. This is where sophisticated analytics and business intelligence can be invaluable in turning the tide.
Sorting the cohort The first step is to identify members who have adherence levels that fall below a user-defined threshold for non-adherence. For these purposes we will use the industry standard, which is 80 percent, and look across three classes of maintenance drugs: anti-hypertensive medications (ACE/ARB), oral diabetes medications, and statin medications for lowering cholesterol. To run the numbers we will use the records of a health plan with 250,000 total members, and only include members who are 18 years of age or older who have not opted out of receiving communications.
Using predictive analytics we discover a baseline noncompliance rate of 22 percent for the hypertension medication, 30.9 percent for diabetes and 25.6 percent for statins. This is our target audience, and the only ones who will receive communication.
Delivering the message To begin changing behaviors, we can use technology to generate and deliver hyper-targeted messages to a total of 13,240 members using their preferred messaging channels (email, SMS/text, print, etc.). The technology also tracks open and bounce rates where possible to help continually refine the program.
Based on the health plan’s prior data, the predictive analytics are able to calculate the program’s impact on adherence, as well as the average medical savings generated for each conversion from non-adherence to adherence. The results are impressive.
In the hypertension category, hyper-targeting messages is able to convert 736 members (more than 15 percent) from noncompliant to compliant. This results in a total savings of $158 per conversion for a total savings of $116,308.
In the diabetes category, 523 members are converted (also more than 15 percent), resulting in a savings of $625 per conversion and $327,113 total.
