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How Analytics Can Help Avoid Member Engagement Misfires


Learn how analytics can be leveraged to hyper-target members for health reminders.

Computer reminder
Robert Oscar

Robert S. Oscar, RPh

One of the standard tropes in any movie showing military battles from the pre-gunpowder era is the line of archers standing at the ready. (See: “Wonder Woman,” “300,” “The Lord of the Rings.”)

When the command is given, they unleash a volley of arrows into the air, which then rain down randomly on the enemy. While some find their marks, the majority are wasted.

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This is similar to what can happen when health plans reach out to members with health reminders focused on preventive care. Historically, reminders such as, “Time for your flu shot,” or “You may need to schedule a mammogram,” have gone out across large swaths of patient populations, with the hope that they will drive at least some to take action that could improve outcomes and lower costs.

While it may be better than nothing, these random volleys of messages are not very efficient-or effective-because they lack context about who the member is, what specific health challenges he or she faces, and what has or hasn’t been done recently to address them.  As a result, these communications can miss the mark, and members may stop paying attention to them.

Leveraging analytics for better member targeting

Increasingly, newer types of analytics solutions are enabling payers to dig deep into member data to build highly precise, hyper-targeted lists that are tailored to the health needs of each specific member. By creating this heightened level of personalization, payers can greatly increase the likelihood that each message hits a target who will find it timely and relevant.

Here’s an example. Typically, payers will send a message to all women within certain age parameters reminding them of the importance of getting a mammogram. It is a general message that doesn’t consider the individual circumstances of each woman.

What about women who have had a double mastectomy? Not only do they not need the mammogram, sending the message could simply serve as a painful reminder of a difficult time in their lives. It could also hurt the relationship between the member and the payer if the members feel the payer doesn’t know them, treats them as a number, and is insensitive to their situation.

Or what about women who have a history of breast cancer in their families? They should receive this message at a much younger age than those who are being targeted generally, since data show they may be higher risk. Leaving them off the list could be a costly mistake for both the member and the payer.

Finally, what about women who had a mammogram last week? Sending a message after the fact makes the payer again look like they don’t know what’s going on, and can even create confusion if members with low health literacy misunderstand and think they must go for another mammogram.

Related article: How Two Health Systems Use Predictive Analytics to Reduce Readmissions

Analytics that are capable of creating hyper-targeted lists can take these and other factors into account, eliminating everyone for whom the message is not relevant or timely to ensure that anyone who receives the message actually needs that information.

Getting your message heard

Personalizing who is targeted in this way not only helps drive action; over time, it trains members to pay immediate attention to a message when it comes in, because they know it contains information they need.

Another way hyper-targeting can be used is to identify members who have co-morbid conditions, inform them of changes they might need to make if a condition is newly diagnosed, and recommend they speak to their physicians. For example, a patient who has asthma who has a heart attack should receive a reminder to follow up with his or her primary care physician. The message would encourage the patient to work with the PCP to ensure that an appropriate type of asthma medication is prescribed and monitored, given the patient’s recently changed heart health history.

Related article: Make the most of predictive analytics: Top tips for health systems, health plans

Analytics can also identify who the physicians are for patients who have had a recent medical crisis and inform the providers of the new findings so they can change prescribing patterns if needed, reach out to reinforce the message, and add a note to the EHR.

Achieving value-based care goals

Today’s consumers have become conditioned to receiving information that is personalized, relevant, and timely in every aspect of their lives-and tuning out everything else. It’s important for payers to keep that in mind as they develop and target member messages.

The days of firing blindly into the air in the hopes of making something good happen are over. By fine-tuning member engagement programs using advanced analytics, payers can ensure that their arrows hit the right targets in order to drive engagement and desired behaviors-and achieve their value-based care goals.

Robert S. Oscar is CEO/president of RxEOB, an industry leader in member engagement applications for pharmacy benefits.

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