One of the key factors that separates championship coaches from those who never reach the summit is their ability to get the best out of each athlete on their teams
They work hard to get to know their players so they can understand what motivates each of them, from how they interact with the athletes during “learning moments” to the goals they set for them to the rewards they offer for reaching those goals.
In a perfect world, physicians and other healthcare professionals would take the same approach. They would get to know where each of their patients live, what’s important to them, how they like to be communicated with, etc., so they could develop an effective strategy for motivating the behaviour changes, such as filling and taking medications — as prescribed — that lead to healthier outcomes.
Unfortunately, there is one huge difference between sports teams and patient panels. In professional sports, coaches only have to get to know a few dozen players at most, and they have nearly unlimited time to spend with them. Healthcare professionals, by contrast, may have hundreds or thousands of patients in their patient panels, and are often given just 15 minutes per encounter to interact with patients.
Tough to get to know anyone well in only 15 minutes, much less patients with complex health issues – even if you’re seeing them on a monthly or weekly basis. With the World Health Organization (WHO) predicting a global shortage of 12.9 million healthcare workers by 2035, this situation is only expected to worsen as fewer healthcare professionals will be available to cover a growing patient population.
There is another way to develop a deeper understanding of patient challenges and motivations so they can be “coached up” more effectively, however. Life sciences organisations can use behavioral analytics to pull big data from multiple sources, build personas that represent large cohorts of patients who share similar characteristics or attributes, and then share them with their physician customers.
These patient personas can help life sciences organisations understand what motivates certain types of people in the aggregate so they can develop programmes that encourage compliance with prescribed medications.
This same framework can also be applied to determine the propensity for certain conditions to manifest themselves in a given area or among a particular group, offering guidance to commercial effectiveness teams on where to concentrate their efforts to maximise financial performance.
The concept of using personas to represent significant segments of the population has been used in other industries, particularly those that are consumer facing, for years. They generally incorporate a broad swathe of data, such as geographic/postal code, socioeconomic status, estimated income, age group, gender, ethnicity and other factors to guide product development, marketing approaches, product mixes in particular regions, and other factors.
In healthcare, building personas becomes more complicated. Healthcare analytics typically rely on only two sources: claims and clinical data. Both are readily available, but they only provide a limited view of facts around a patient’s health and use of healthcare. They offer very little about who patients are, the environment they live in, their actions or what motivates them.
To gain that understanding, the analytics must also bring in that socioeconomic and behavioural data to create a holistic, 360-degree view of the patients. This more complete view helps life sciences organisations understand the factors and influences that can have a profound effect on their ability to successfully encourage patients to become engaged in their own care, as well as which products should be the focus of discussions with healthcare professionals.
One of the most significant examples is the effect of postal codes on outcomes. People who live in a particular area tend to have similar levels of income and education, two key factors in predicting whether a patient will adhere to a particular plan of care such as taking medications.
Those in lower-income areas may not have the disposable income to pay £500 per month for medications, no matter how badly they are needed. They are unlikely to adhere to the regimen and, thus, will not have the desired outcomes.
For commercial effectiveness teams with a lower-cost alternative at their disposal, this situation represents an opportunity to gain market share by offering an option to physicians that will solve a problem for them. It also helps them avoid selling a product that will not perform as expected.
Post code information combined with credit card data can also shed light on how amenable certain diabetic patient personas are to heed diet and exercise instructions that can have an effect on the outcomes of treatments. For example
In short, patient personas can provide a distinct advantage anywhere there is a statistically large enough group of people for whom a detailed, 360-degree view of information can be obtained.
Socioeconomic, psychographic, gender, healthcare utilisation, and many other types of data can generally be acquired through partnerships (such as those between payers and life sciences organizations) or by purchasing them.
Attitudinal data, which plays a large part in predicting a successful course of action that improves outcomes, can be more challenging. For example, some people don’t like to go to their primary care physician (PCP) because they fear bad news. That type of data isn’t available through typical sources.
Gathering this type of attitudinal data often requires participants to answer survey questions. Yet, this is often an unreliable source. Take the simple example of a survey of New Year’s Resolutions. Data gathered from this survey might indicate that 50% of those surveyed plan to lose at least 20 pounds by eating less and exercising more. But if you measured their results at the end of that year, it is likely that the number who reached that goal fell far short of the total.
Still, gathering attitudinal information through surveys and a basic understanding of the social fabric can at least begin to factor into decisions — especially if the answers can be tied to other behaviors, such as the parents of asthma patients filling prescriptions.
This information can help determine the patient’s “impactability” and especially “intervenability.” Impactability is the likelihood that a particular intervention or plan of care will reduce risk and improve outcomes. Intervenability is the likelihood that the patient will follow that plan.
With sufficient behavioral data to create a 360 degree view for these personas, and the proper algorithms, both factors can be assigned a risk score that helps healthcare and life sciences organisations work together to determine how best to help each patient. It can also help them determine where to assign their human and financial resources to maximise ROI.
The first step in building effective patient personas is to acquire and normalise the required sources of data so they can be used within the behavioral analytics application. The data is the ready to be analysed to bring out the facts that are most important – the data that binds certain groups of people together, as well as the factors that separate them.
The goal is to ensure the similarities within the group are as close as possible, whereas the difference between each group are distinct. This type of analysis involves going beyond traditional demographic age breaks to determine which factors are most important to the behaviours of each group.
For example, a typical demographic age range might show one group that is 18–35, a second that is 36–49 and a third that is 50–64. Yet the analysis for these groupings may show little change in behaviours or attitudes between the second and third groups. In that case, it makes more sense to view them as a single entity and then base their personas on other factors where the differences are greater – such as their risk level, highest education level achieved and/or estimated income.
Whatever technical form of analytics are used, the key is to ensure that each persona presents an intelligent, cohesive view of all those within it while remaining distinct from other personas. In other words, build them so that each individual is only a proper fit in a single persona. If too many fit into more than one persona, the program requires more work.
One way that life sciences organisations can use this technology is to develop patient personas that classify undiagnosed diabetics, and then compare behavioural information across the nation to determine the best markets to target to increase sales of diabetic treatments.
Sharing this information not only helps to boost revenue. It also provides a value-add from the pharmaceutical company to the providers. This allows all parties to work toward their mutual goal of bringing uncontrolled diabetics under control in shared-risk arrangements.
The one caveat to keep in mind is that personas are not set in stone. As organisations gather more data and learn more about what works and what doesn’t, they must constantly refine their current personas, add new ones or remove existing personas if needed, and move members within them to ensure they have the most accurate 360 degree view.
Coaches who understand what motivates each of their players, from the team’s stars to the role players who can suddenly become very important in a given situation, are the ones who tend to win championships year after year./p>
In the same way, life sciences organisations that gain a deeper understanding about who patients are, what their needs are, and what motivates them, will find themselves in a position to deliver meaningful help to physicians while realising greater ROI. Personas that offer a 360-degree view based on behavioural analytics are the key to achieving health outcomes victory.