How analytics can help avoid member engagement misfires

How analytics can help avoid member engagement misfires

By Robert Oscar, R.Ph.

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, etc.)

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.

This is similar to what typically happens when health payers prepare communications to their members. Payers send generic messages, such as “Time for your flu shot” or “You may want to schedule a mammogram,” across large swaths of different populations, hoping they will drive at least some members to take actions that will improve their health (and lower payer 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 they face, and what they have or haven’t done recently to address them.  As a result, they largely miss the mark.

Fortunately, a new class of analytics enables 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 ensure each message “arrow” hits a target who will find it timely and relevant.

Here’s an example. Typically payers will send a message to all women over a certain age 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, is insensitive to their situation, etc.

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 the data shows they are more susceptible to that particular disease. Leaving them off the list could be a costly mistake for both the member and a payer.

Finally, what about women who had a mammogram last week, after the initial list was run? 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.

Analytics that are capable of creating hyper-targeted lists will take these and other factors into account, eliminating everyone for whom the message is neither relevant nor timely to ensure that anyone who receives the message actually needs that information. 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 would be to make recommendations to co-ordinate with a primary care physician after a myocardial infarction for an asthma patient.  The message would encourage outreach needed for the physician to co ordinate with the patient in order to ensure that the appropriate medication is prescribed and monitored.

The analytics can help identify those members who have these co-morbid conditions, inform them of the new information, and suggest they speak to their physicians about making the change. The analytics can also identify who the physicians are for those patients and inform them of the new findings so they can change their prescribing patterns generally, reach out to their patients who qualify to reinforce the message, and add a note to their electronic health records to speak with these patients about making the change should they come in for a visit on another subject.

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. Payers must keep that in mind as they develop their own member messages.

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