How Will Patients Behave?

It’s critical for pharmaceutical companies to understand their consumer audience: not just how sick they are or how well they recall advertising or how often they visit particular Web sites. Marketers need to know what factors affect the way that different groups seek and use healthcare information, and how this translates into scripts and compliance. And they need to be able to identify these groups. Only then can they address the needs of different segments of their target audience with specific direct-to-patient messages.

While consumers’ overall health is somewhat predictive of their motivation to seek health information, their attitudes and values about health are consistently more useful in predicting how they are going 

to behave. This is the fundamental finding of this study of US consumers’ preferences for seeking health information, commissioned by MRxHealth in conjunction with MM&M.

The survey of 546 consumers was carried out in two parts: Phase I focused on consumers’ motivations behind looking for information, the sources used, the frequency of searching and how the information was used. Phase II compared the learning styles and language preferences of the groups identified in Phase I. The research also yielded a comparison of different information sources, including reach, frequency of use and trustworthiness, along with their ability to motivate a conversation with a doctor and instigate a prescription request.

Respondents were asked to rate their overall health and as a result were divided into four distinct groups: Poor Health, Low-Average Health, High-Average Health and Good Health. Information-seeking motivation, behaviors and uses differed among these four groups.

Motivation: Those with High-Average or Good health are more likely to seek to maximize their health. Those with Poor or Low-Average health tend to look for information on a situational basis, when they (or a friend or family member) experience a problem.

Frequency: Those with Poor health look for information more frequently, with the majority reporting at least once a week. Most of those in the Low- and High-Average group look for information at least two or three times a month, while those in Good health look at least once a month. This suggests that necessity drives frequency. The Low-Average and Good groups are more likely than the others to report that they never look for health information; this implies that disinterest in health can be both attitudinal and situational.

Doctor relationships: High-Average and Good health groups report better relationships with their physicians than the Low-Average group on every element measured. Surprisingly, those with Poor health also report better doctor relationships than the Low-Average group on three of the five measures.

Persistence: Poor and Low-Average groups are one and a half times more likely to report that they stopped taking a prescription drug without talking to their doctor. The primary reason for stopping treatment differs: the Poor and Low-Average groups report side effects, while the High-Average and Good groups cite improvement in their conditions.

Information uses: Poor and Low-Average groups are more likely to have spoken to their doctors about the information they found. Those with Poor health are also three times more likely to have asked their physician for a specific medication. For all four groups, there is an 87% likelihood that, having requested a specific drug, a patient receives it.

“If patients are going to get the drug they asked for, regardless of the channel that got them there,” says Cheryl Lubbert, president, MRxHealth, “then marketers really need to get patients to talk about a specific product name.” Lubbert suspects that doctors’ willingness to fulfill script requests stems from a desire to maintain positive relations with patients. Plus, she adds, physicians may not have the ability to differentiate the products sufficiently.

Openness to advertising: Those with Poor health are more interested in ads than those in the other groups. The Poor and Low-Average groups are more likely to recall specific products named in the ads. The Poor group is also more likely to say that ads for Rx drugs are helpful.

Overall health, while generally predictive, is not as predictive as the multidimensional PATH model (Profiles of Activities and Attitudes Towards Healthcare). PATH is a systematic approach to revealing otherwise unseen patterns of healthcare-related behaviors and attitudes—archetypes— among adults that shape the healthcare outcomes we see.

The PATH model segments the sample into nine groups (or archetypes) defined by responses to statements that assess an individual’s reaction to 11 health issues (see “The 9 Patient Archetypes,” opposite).

“The purpose of the segmentation is to define an audience in a way that you can address them, so information can be tailored to them,” explains Jo Anne Jensen, VP market research, MRxHealth. “While it’s useful to know that a sick person is more likely to want your information, that’s not really actionable.”  

Motivation (Fig. 1): Clinic Cynics, Avoiders and Traditionalists are least likely to seek healthcare information to maximize their health, while the Independently Healthy, Ready Users and Naturalists are more likely to have this motivation. This is consistent with their known traits. Clinic Cynics, Avoiders, Generics and Traditionalists tend to look for information and react to problems as they arise. Ready Users and the Independently Healthy express a general interest in health and are more proactive. Traditionalists are the most likely to admit that they don’t actively look for health information. Clinic Cynics, Avoiders and Loyalists are similar in that they fail to seek out health information but also admit to ignoring it when they come across it. These findings are consistent with past studies and the PATH archetypes.

Frequency (Fig. 2): Ready Users and the Independently Healthy tend to look for information more frequently than the others, with the majority seeking information at least two to three times a month. At the other end, Clinic Cynics and Avoiders rarely or never look for information. Traditionalists and Loyalists look for information once a month or less, while Generics, the Family Centered and Naturalists look two or three times a month or more. These findings demonstrate that information-seeking behaviors are not linked to a single set of healthcare consumer traits but to multiple combinations of traits. 

Doctor relationships: The trends follow those typically found: Clinic Cynics, Avoiders, Generics and Unassigned patients are more likely to see a physician once or less a year, while Traditionalists, Loyalists, Ready Users and the Independently Healthy are more likely to see a physician two or more times a year. On the whole, the doctor relationship is weakest among the Clinic Cynics, Avoiders, Generics and Naturalists. It is strongest among the Family Centered, Ready Users and the Independently Healthy.

Persistence: Clinic Cynics and Naturalists are significantly more likely to have stopped taking medication without talking to their doctors. Their rates are 62% and 56%, respectively, compared with the average rate of 37%. Both these segments indicated that the primary reason for discontinuing medication was side effects. Avoiders, Traditionalists and the Independently Healthy, on the other hand, cite an improvement in their conditions as the primary reason for stopping treatment. Generics, Loyalists and Ready Users most frequently cite side effects, while the Family Centered are equally split between side effects, expense and improvement in their condition.

The effects of undisclosed distrust and expense on persistence and adherence: Even though Clinic Cynics and Naturalists attribute their high rates of discontinuation to “side effects,” there is clearly another undisclosed factor at work. Both Clinic Cynics and Naturalists display higher levels of distrust of healthcare providers and lack confidence in the competence of medical professionals. When comparing the rates of discontinuation across the various levels of distrust, the effect is clear (Fig. 3). As lack of faith in the competence of healthcare professionals increases, so does the rate of dropping medications, particularly at the most extreme levels of distrust. It’s worth noting that lack of trust in the competence of medical professionals is, for the most part, an undisclosed and untreated complication interfering with adherence and persistence. Trends show that the lack-of-confidence issue can plague 17% to 33% of healthcare consumers. Left untreated, undisclosed distrust will play havoc with efforts to improve adherence and persistence.

Consumers who avoid seeking care due to the expense are also more likely to discontinue taking a prescription medication without talking to their doctor (Fig. 3). For those who say they avoid seeking medical care due to the expense, 46%–50% say they have stopped taking a medication without talking to their doctor. The issue of costly medication is not likely to go away, and based on the PATH measures, the propensity to avoid seeking medical care due to expense affects adherence and persistence for approximately 40% of adults.

But patterns of behavior and attitudes do not exist in a vacuum. Bivariate analysis of the propensity to avoid seeking healthcare due to the expense, and the conviction that healthcare professionals are not competent, confirms that one of these factors affects the other. And combined, their effect is more severe—80% of patients who are at the extremes of avoiding healthcare due to the expense and believing that healthcare professionals are not competent admit to discontinuing a medication without talking to their doctor. 

Information uses (Fig. 4): Loyalists, Ready Users, the Independently Healthy and Naturalists are more likely than the others to have spoken with their doctors about information they found. Avoiders are the least proactive, being only half as likely to have spoken with their doctors. This behavior is consistent with their other patterns. 

There are few differences between segments based on asking a doctor for a specific medication; only Naturalists are significantly more likely to do so. There is evidence that physicians differentiate between segments, however, granting the request for a specific medication less frequently to Clinic Cynics and the Family Centered.

Openness to advertising (Fig. 5): Ready Users, Loyalists, the Independently Healthy and Generics are most interested in prescription drug ads. However, only the Independently Healthy have a good recall of the product named in the last ad they remember seeing.

Word preference exercise (Fig. 6): Respondents were asked to state their preference between several combinations of two words. Seven of the word pairings showed statistically significant differences in preference across the PATH archetypes—consistent in every case with the healthcare priority profile defined by the PATH model. For example, Ready Users, the Independently Healthy and Naturalists preferred nutritional over tasty, consistent with their greater focus on health and nutrition. Similarly, there was a marked preference for the word illness over disease among Avoiders and Naturalists. This is reflective of how these groups want to think about healthcare problems; an “illness” is potentially less harsh and less well defined in terms of effects and outcomes; but a “disease” is more often “medically” defined with expected outcomes and recommended treatments.

Learning style exercise: Respondents were exposed to a description of the physiology of an allergic reaction, representing a moderately complex topic. The information was presented in three different forms: as a written description, as a graphical interactive object and as an audio recording of a doctor explaining the information to a patient. After either reading, viewing or listening to the content, respondents were asked to answer a series of questions about the information they had absorbed. 

Overall, respondents answered more questions correctly when exposed to the audio version of the content (Fig. 7). Those who were given a written description scored next highest, while those who were shown the graphic interactive learned the least. The ability to understand health information varied by PATH segment. Surprisingly, Avoiders scored highest, followed by Ready Users, the Independently Healthy and the Family Centered. Loyalists, Clinic Cynics and Traditionalists scored lowest.

When exposed to the audio format, all segments did well, with Naturalists scoring highest. When exposed to text, however, only the interested groups scored well: the Family Centered, Ready Users and the Independently Healthy. The graphic interactive format was only successful in communicating to Avoiders and the Independently Healthy.

Information sources (Fig. 8): The perceived trustworthiness of health information sources clearly varies between channels. But the actions patients take regarding these different channels doesn’t necessarily correlate with the level of trust. For example, the Internet has a lower trust score (3.67) than Pharmacists (4.25), yet it drives a greater proportion of people to ask for a specific drug (34% vs. 27%).

When the “Spoke to Doctor” figure is high and “Asked for Rx” is low, a missed marketing opportunity is identified. For example, with the Internet, while only 62% of people have spoken to their doctor about information they found online, fully one third have asked for a specific prescription drug—that’s a high conversion ratio. Conversely, in the case of Other Health Professionals, while 81% of the people who talked to a nurse or somebody else, and later had a conversation with their doctor, only 17% asked for a specific prescription drug—this implies that companies are missing an opportunity by not marketing sufficiently to “other” healthcare professionals.

“Many sources that are highly motivating and very credible to consumers are going underutilized by pharma marketers,” confirms Jensen. 

Perhaps the most glaring of these is In-Person Support Groups: 94% said they spoke to a doctor as a result of content at an in-person support group, but only 4% requested a specific drug. “When somebody speaks to another person directly whom they don’t see as having a vested interest, it’s very motivating,” adds Jensen. “Support Groups is the most motivating channel but the least productive in generating scripts. So the more information you can get out to patients, the better (such as ‘This drug worked for me’ or ‘I recommend this’). This would certainly support the trend toward word-of-mouth marketing in healthcare.”