The promise and anticipated value of digital health is massive. Total funding for new digital health tech reached a record $5.8 billion in 2017, and $180 million was invested in just the first week of May 2018, per Rock Health.
One reason for the investment might be the strong belief in the the power of health tech to change healthcare. After all, the tendency toward technology chauvinism — the perception that technology in itself is the solution to the problems we face — seems prevalent in digital health. The problem is that it might be undermining true health innovation and the next generation of digital health entrepreneurs.
We need to aim to solve real-world problems to create disruption.
The reason for the lack of disruption in healthcare is that very few digital healthcare solutions are built on intimate knowledge of the underlying, behavior-driving values of intended users. Most health tech solutions are not created to solve problems identified by consumers or patients. Instead, most digital health products are built to solve issues defined by healthcare professionals or built by engineers and for engineers because a new technical invention made it possible.
But innovation is something quite different from technological inventions or scientific discoveries. Innovation means to bring new value to the market. This happens when technology can make a difference in real people’s lives and solve the problems they experience, on a large scale.
So how do we identify what people need out there in the real world so we can build the next generation digital health solutions that resonate with target users, solve actual problems and have the potential to disrupt markets?
We know from social science that people rarely are able to clearly articulate what they need. If Henry Ford had asked people what they wanted, they would have asked for a faster horse, or so the saying goes. We also know that patients often define their health-related challenges differently than their doctors do. In short, users’ perceptions and choices in health tech often differ radically from what health tech entrepreneurs expect.
Social scientists talk about habitual blind spots: the perspectives and areas of life outside our subjective view that we don’t even know we can’t see. In health tech, habitual blind spots can interfere with commercial breakthroughs because they cause us to build solutions that solve the wrong problems.
We have to build solutions based on real world insights.
Most people in health tech claim to be patient-centric, thinking that the ‘human aspect’ is covered by UX design or by doing a focus group to tweak and test final concepts. The average digital health startup employs tech specialists, clinicians and maybe a few UX designers. This is considered an interdisciplinary team fully capable of making a digital health tech startup successful.
But these professional groups are not trained to identify human needs that tend to be much more complex when it comes to health than, say, listening to music or order food online. Letting engineers “shadow” users or accumulating extensive feedback from various provider databases are approaches that fall short.
To create true innovation in digital health we need more sophisticated methods to understand the healthcare needs of the individuals. It is not a substantial methodology for entrepreneurs to venture out to ‘talk to users’ or rely on their own private experiences as patients, caregivers or doctors.
Investing up-front resources in insights about the real world prior to product conceptualization is rarely considered by digital health entrepreneurs. The quick-to-market attitude is that since marketing research or the medical community have established a hole in the market, why bother doing extra costly research?
But the fact that more than $1 trillion is spent per year on chronic diseases, per the CDC, that in many cases could be alleviated with personalized digital health solutions, is a clue that something is wrong. The yet-to-happen revolution of health outcomes suggests that firms could benefit from investing in more adequate knowledge of patient populations to achieve more successful innovation.
Health behavior is structured by cultural context and complex socio-economic determinants of health and is not easy to understand. But research in controlled environments is especially inadequate to help us understand what a meaningful solution to our users will be, in the context of their daily life.
What we need instead are insights from the real world that help us understand people’s behavior in context of their daily lives to be able to engage them as users. Such real-world insights are established using rigorous social science, gathering observational data on the daily life and disease experience of target users.
Real-world insights form the basis for how to conceptualize a health-tech solution if it is to gain traction, create real behavior change and actually disrupt the market. Real-world insights lay the strategic framework for feature-, interaction-, service- and device-design to ensure every aspect of a health-tech solution solves the right problem.
Social scientists specialize in systematic and rigorous approaches to uncover underlying drivers of human behavior, factoring in socio-economic and cultural conditions. Professionals such as digital anthropologists and health sociologists have methodologies to establish an actionable understanding of challenges facing health tech’s target users. Their work radically increases the likelihood of successful health-tech innovation, but they are rare in health-tech teams.
True digital health innovation depends entirely on such foundational real-world insights being represented in every decision-making step throughout product development.
Designing beautiful and delightful features and experiences is not enough if the core value proposition is not derived from a deep understanding of what products users actually need.
Before we begin designing a solution in digital health, it is paramount to make sure we truly understand what drives people’s behavior and what their emerging needs are. Otherwise we risk that the next generation of health entrepreneurs think that tech in itself is a solution.
In that context, let’s consider three assumptions that prevent digital health breakthroughs below:
- Behavioral change is a linear process, and that getting people to act differently is simply is a matter of educating them and providing them with enough data. It is commonly assumed that when people know what is medically right for them, or have enough data about their situation, they will make rational deductions, follow medical advice and behave in a way that improves their situation. This is false. Social science has taught us that human beings are irrational: our behavior is influenced by cultural beliefs, social norms, moods and emotions.
- Personal experiences are valid and reliable data sources. Since many digital health entrepreneurs are motivated by personal experiences they often infer that those experiences are also a valid data source for business decisions. Underlying this is a false perception that social determinants of health don’t exist and that everyone has a similar health behavior despite different cultural context and socio-economic factors.
- People are fully aware of their values, perceptions and beliefs, and that they are capable of explaining reasons for their behavior and have full cognitive access to what drives their actions. This falsehood leads to the misconception that simply hosting a few focus groups and asking people what they want is all the research needed in early development of successful digital health solutions.
In order to get next-gen digital health right, entrepreneurs need to critically assess what they know about their intended user group, where their data and insights come from and how they might be improved. Second, they should never accept a simple hassle-map of current solutions in the market as substantial enough to identify health tech opportunities.
Behavior change is the key to disruption and these steps will help digital health deliver on its grand promise of being able to solve actual problems.
Photo: CherriesJD, Getty Images