Addressing Unspoken Fear in Healthcare Marketing

There’s a lot of fear in healthcare marketing — the unspoken fears that lurk in the minds of consumers, blocking their ability to absorb your content. Marketers who don’t address these nagging worries in the conversion funnel risk turning off prospects who are otherwise excellent candidates for that service line.

Healthcare Marketing Strategy
Credit: Pixabay by Gerd Altmann

There’s a lot of fear in healthcare marketing. I’m referring to unspoken fears that lurk in the minds of consumers, blocking their ability to absorb your content. Marketers who don’t address these nagging worries in the conversion funnel risk turning off prospects who are otherwise excellent candidates for that service line.

Fear is a funny thing. A little of it keeps you alert and causes you to be more careful. Too much fear and you cognitively shut down. The difference — in the first scenario you believe you can do something to minimize the danger, while in the other scenario you don’t know of a solution and you feel paralyzed.

Imagine marketing a service line for a serious health condition. Your reader may have insurance, but there’s a negative inner dialogue unfolding in his mind: “I will miss work. If I miss too much work, I might lose my job. If I lose my job, I won’t have life or health insurance. If I don’t have insurance or a job, I could bankrupt my family. They would end up with nothing.”

The prospect has catastrophized a possible outcome and now wants to avoid your messaging entirely out of both fear and guilt. And because these internal monologues are unspoken, it’s very difficult to get the kind of feedback that enables you to make improvements. If you have service line campaigns that are not performing, ask yourself if fear might be getting in the way and how you can break down those barriers to conversion.

You can address unspoken fears at several places along the funnel, starting right at the top and adding more detail along the consumer journey:

  • At the top of the funnel, consider adding a truthful, positive indicator into your outbound messaging that contrasts today’s treatment with what was available years ago. Advances in knowledge, techniques, and technology can help a fearful consumer move beyond legacy emotional assumptions and create a narrow window of reconsideration.
  • On your campaign page, proactively address common concerns while also streamlining navigational flow to your call-to-action. A generic FAQ link may be too subtle for consumers with nagging worries. Consider clearly labeled links such as “Time away from work,” “Insurance accepted,” “How outcomes have changed,” “Managing out-of-pocket costs” or similar topic-specific labels. Each item or grouping should conclude with your CTA.
  • System-generated emails triggered by user submissions as well as nurture campaigns should include links to content that normalizes typical concerns and provides reassurance that these can be discussed comfortably at the appointment. Some patient no-shows are caused by nagging worries that cause people to disengage even before an in-person consultation.
  • Consider adding a simple form at check-in that asks about the patient’s concerns and provides pre-populated topics to select. Patients can become surprisingly quiet when the doctor enters the room. If the provider knows what topics are weighing on the patient’s mind, the dialogue can be more meaningful and a foundation of trust developed.

And throughout this process, work with your organization’s best-performing providers as well as financial counselors, patient navigators, social workers and philanthropic foundation for insights that help improve responses to common patient concerns.

Direct Marketing: An Rx for Medication Non-Adherence, Part 2

Last month, I wrote about the fact that regardless of the condition for which a medication is prescribed, after three to four months, only about 40 to 50 percent of the people prescribed long-term medications are still taking them. Controlled testing has shown that direct marketing techniques can improve patient adherence with medications by 20 to 25 percent. So why aren’t these techniques used more often?

healthcare marketing[Editor’s note: In a related matter, healthcare marketers are invited to discuss matters like these on June 15 at the Target Marketing Healthcare Roundtable. This link allows marketers to register.]

Last month, I wrote about the fact that regardless of the condition for which a medication is prescribed, after three to four months, only about 40 to 50 percent of the people prescribed long-term medications are still taking them. Controlled testing has shown that direct marketing techniques can improve patient adherence with medications by 20 to 25 percent. So why aren’t these techniques used more often?

One of the biggest barriers to creating effective patient intervention programs is the speed with which they have to be implemented. Keep in mind that 20 to 30 percent of prescriptions are never filled, and 40 to 50 percent are not taken as prescribed, according to the National Council on Patient Information and Education. (Opens as a PDF) As a result, the closer to the point of prescribing that patient interventions begin, the more successful they will be. If the interventions don’t start until three months after a prescription is written, a significant portion of the patient population is already gone, and the patients who remain are those who are likely to remain persistent, anyway.

There are several reasons why people don’t take their medication as prescribed, but most important among them is the fact that people often do not understand why they are taking a particular medication or how long they’re supposed to take it.

Educating patients about how their medication works in simple language can go a long way to helping them realize how and why to take it. And while there are various stakeholders who can benefit from increased adherence, each has their own particular barriers to creating an effective program.

First, there are the pharmaceutical manufacturers. They’re constrained by the FDA regarding what they can say to the patient, not allowed to stray from the language in their approved labeling. Frequently, this constraint ends up with patients getting communications in language that they can’t understand. Additionally, brand managers may be reluctant to spend money from this year’s promotional budget to affect potential sales increases in future periods when they may have moved on to another assignment.

Pharmacies could potentially benefit from increased adherence; however, their margins on prescription drugs are too low to devote the resources necessary to deploy an effective patient education program. Their efforts are largely limited to refill reminders, which are not effective in increasing persistency. Forgetfulness affects compliance (taking medication as directed), but persistency (continuing to take medication over time) is driven more by the psychological factors I addressed last month.

Healthcare providers are constrained by the amount of time they have to spend with a patient and by the fact that the patients generally forget most of what they’re told during their 10 minutes with the provider before they even leave the office.

Those who have the most to gain are the insurers, the ones who pay the healthcare bills. Apart from the patients, these payers have the highest stakes in the game. Convincing a patient to take cholesterol and blood pressure medications is a lot cheaper that paying for the hospitalization costs associated with a cardiac event. And while the insurer, (for example, Aetna) may know that a patient was diagnosed with a particular condition, it’s the PBM, pharmacy benefits manager, (for example, Express Scripts) who knows if a medication was dispensed and when. These are important data points for creating effective patient interventions, because the sooner you get to the patients, the more patients you can affect. But the PBM can’t account for the 20 to 30 percent of prescriptions that are written and never filled.

The most effective interventions start with incenting patients at the time that their initial prescription is written: specifically, giving them a free initial supply of medication for providing their contact information. That data capture lays the groundwork for early intervention. From there, a data sharing partnership between the payers and the PBMs can provide the information to get the appropriate communications to patients at the right times. This type of partnership is a tall order, but as the New York Times reported in April — citing a study in the Annals of Internal Medicine:

“This lack of adherence, the Annals authors wrote, is estimated to cause approximately 125,000 deaths and at least 10 percent of hospitalizations, and to cost the American health care system between $100 billion and $289 billion a year.” (Opens as a PDF)

The various stakeholders need to come together to improve this situation.

Patients Aren’t Ready for Treatment?

The key is to an effective prescription is to listen to the client first. Why do they lose sleep at night? What are their key success metrics? What are the immediate pain points? What are their long-term goals? And how would we reach there within the limits of provided resources

In my job of being “a guy who finds money-making opportunities using data,” I get to meet all kinds of businesspeople in various industries. Thanks to the business trend around analytics (and to that infamous “Big Data” fad), I don’t have to spend a long time explaining what I do any more; I just say I am in the field of analytics, or to sound a bit fancier, I say data science. Then most marketers seem to understand where the conversation will go from there. Things are never that simple in real life, though, as there are many types of analytics — business intelligence, descriptive analytics, predictive analytics, optimization, forecasting, etc., even at a high level — but figuring what type of solutions should be prescribed is THE job for a consultant, anyway (refer to “Prescriptive Analytics at All Stages”).

The key is to an effective prescription is to listen to the client first. Why do they lose sleep at night? What are their key success metrics? What are the immediate pain points? What are their long-term goals? And how would we reach there within the limits of provided resources and put out the fire at the same time? Building a sound data and analytics roadmap is critical, as no one wants to have an “Oh dang, we should have done that a year ago!” moment after a complex data project is well on its way. Reconstruction in any line of business is costly, and unfortunately, it happens all of the time, as many marketers and decision-makers often jump into the data pool out of desperation under organizational pressure (or under false promises by toolset providers, as in “all your dreams will come true with this piece of technology”). It is a sad sight when users realize that they don’t know how to swim in it “after” they jumped into it.

Why does that happen all of the time? At the risk of sounding like a pompous doctor, I must say that it is quite often the patient’s fault, too; there are lots of bad patients. When it comes to the data and analytics business, not all marketers are experts in it, though some are. Most do have a mid-level understanding, and they actually know when to call in for help. And there are complete novices, too. Now, regardless of their understanding level, bad patients are the ones who show up with self-prescribed solutions, and wouldn’t hear about any other options or precautions. Once, I’ve even met a client who demanded a neural-net model right after we exchanged pleasantries. My response? “Whoa, hold your horses for a minute here, why do you think that you need one?” (Though I didn’t quite say it like that.) Maybe you just came back from some expensive analytics conference, but can we talk about your business case first? After that conversation, I could understand why doctors wouldn’t appreciate patients who would trust WebMD over living, breathing doctors who are in front of them.

Then there are opposite types of cases, too. Some marketers are so insecure about the state of their data assets (or their level of understanding) that they wouldn’t even want to hear about any solutions that sound even remotely complex or difficult, although they may be in desperate need of them. A typical response is something like “Our datasets are so messy that we can’t possibly entertain anything statistical.” You know what that sounds like? It sounds like a patient refusing any surgical treatment in an ER because “he” is not ready for it. No, doctors should be ready to perform the surgery, not the patient.

Messy datasets are surely no excuse for not taking the right path. If we had to wait for a perfect set of data all of the time, there wouldn’t be any need for statisticians or data scientists. In fact, we need such specialists precisely because most data sets are messy and incomplete, and they need to be enhanced by statistical techniques.

Analytics is about making the best of what we have. Cleaning dirty and messy data is part of the job, and should never be an excuse for not doing the right thing. If anyone assumes that simple reports don’t require data cleansing steps because the results look simple, nothing could be further from the truth. Most reporting errors stem from dirty data, and most datasets — big or small, new or old — are not ready to be just plugged into analytical engines.

Besides, different types of analytics are needed because there are so many variations of business challenges, and no analytics is supposed to happen in some preset order. In other words, we get into predictive modeling because the business calls for it, not because a marketer finished some basic Reporting 101 class and now wants to move onto an Analytics 202 course. I often argue that deriving insights out of a series of simple reports could be a lot more difficult than building models or complex data management. Conversely, regardless of the sophistication level, marketers are not supposed to get into advanced analytics just for intellectual curiosity. Every data and analytics activity must be justified with business purposes, carefully following the strategic data roadmap, not difficulty level of the task.