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Patient engagement has been called the blockbuster drug of the century. In the booming frontier that is digital health everyone is trying to activate and sustain end users. Despite digital health funding and healthcare consumerism advancing at a faster-than-ever pace, abandoment statistics reveal that approximately 50% of people who start using a digital health device stop using it within six months. Certainly more efficacy data is needed, but why, with such a proliferation of digital health solutions, are engagement trends so poor?
Because partnerships between technology developers, behavioral scientists and designers for better healthcare is new. And very much needed.
Learning from Failure
In 2005, while doing my doctoral work at Columbia University’s Department of Health & Behavior Studies, I embarked on a year-long project to develop an online exercise motivation program. I was positioned full-time at Go Ask Alice!, Columbia’s Health Education Program, and it was my job to leverage technology for the increase of physical activity among university faculty and staff.
Taking everything I knew about health behavior change theory, I partnered with technology leaders and engineers to build a personalized dashboard that enabled users to log their exercise time. We called it “The 100 m.i.l.e. Club” (Minutes I Logged Exercising). Users could plan, track and see their exercise progress. The idea was simple: any user that performed and logged 100 minutes of physical activity per week would be entered into a lottery to win a small prize like a t-shirt or water bottle.
It was a big failure. Or rather, a typical digital health failure. We saw a surge of sign-ups within the first month. Then, new users dwindled….fast. Most users remained active on their dashboards for 2-3 weeks, then fell off.
I was stumped. We did everything “right.” We spent significant time and money, and worked hard to ensure behavior change theories were at the core of our development decisions, yet still, attrition rates were 60+%.
Designing for Behaviors
After experiencing and observing many tech-enabled heath behavior change failures and successes over these last 10 years, I realized one of the biggest reasons “The 100 m.i.l.e. Club” (100MC) failed was because we developed for an outcome.
Our goal was to “enable people to exercise more.” That’s a great goal. But we did not do any strategy to specify exactly how that would happen.
We should have designed for behaviors:
- Know your humans. Understand the people you are designing for before you build anything. User research sheds light on how, when, and why people behave. We did zero user research during the 100MC project, and as it turned out, our users were not incentized by a free water bottle. They wanted to meet other people at the university to exercise with, and nothing about our platform connected them to each other. Find out what your users want and prioritize behaviors accordingly. Empathize with their feelings and motivations. Write a Point of View (POV) statement to clarify their needs. Had we known our users for the 100MC, a POV statement might have been Columbia University staff members need to connect with other staff members because they want people to exercise with during their workday.
- Define target behaviors. The very definition of ‘engage’ is ‘to participate.’ Meaning, to do something. What do you want your users to do? The answer must be concise and small, like “eat one apple every Monday.” If your goal is to help people lose weight, for instance, then list all the really small, specific behaviors that might impact that goal.
I learned how to define a behavior from Dr. BJ Fogg. Currently, one of my favorite tools to use is the Fogg Behavior Grid. It guides you to type a behavior according to familiarity, frequency, intensity, and duration. An example for the 100MC might have been to log exercise minutes on the dashboard immediately after lunch every Tuesday for the first month.
- Trigger users to act. A trigger is anything that tells your user to “do it now.” An external trigger is something – an alarm, e-mail, item, a person – that your user physically interacts with. An internal trigger – a feeling, thought, memory – is inside your user’s head. External trigger design is easier, so experiment with ways to trigger your users regularly to avoid the novelty effect. We triggered 100MC users with e-mails and paper fliers, none of which worked after a few weeks. Figuring out how a behavior can be triggered is critical for user engagement. I primarily source the Hook Model and the Fogg Behavior Model for guidance on trigger design, and always ask my design peers to create the visual (or other sensory) experience.
- Enable behavioral practice. If you want your users to act more than once, then you must find ways to reinforce and reward for repetition. Feedback loops offer one form of reinforcement. Another is to place the behavior in a new and meaningful context. The simpler you make the behavior, the more likely someone will engage. Practice is important because it builds self-efficacy, trust, and opens the door to habit formation. The 100MC didn’t offer users any reason to practice, and the log in process was laborious.
- Experiment! All of this happens with ongoing user research and testing. Experiments are needed to know what works and what doesn’t, so validate your decisions by testing with your users. We launched the 100MC and never attempted a single iteration. Behavior change is dynamic, so digital health solutions must be too.
During development of the 100MC, we kept wondering “can we build it?” The right question would have been “what will happen if we build it?” Forget about features, design behaviors. Tying strategic behavior design to engagement outcomes will lead to more pratical and digitally relevant solutions. Companies outside of healthcare, like Opower and HelloWallet, are doing this very well. Digital health companies doing behavior design are starting to publish efficacy outcomes (like Mango Health, Omada Health, Spire, and Healthvana), Even the NIH encourages technology development be guided by both evidence-based behavioral strategies and user-centered design principles. So gather round ye technologists, scientists, and designers.
I regularly receive e-mails from folks interested in the intersection of healthcare, behavior design, and technology. They write to ask about my career path and how they might learn more about this professional sweet spot.
Below are my top recommendations for learning more about digital healthcare behavior design:
This does not include academic publications nor the many countless articles that I believe are super useful; mostly because this list is intended for newcomers to familiarize themselves with the contemporary dialogue.
When the Affordable Care Act first rolled out, I wrote about the spike in emergency room visits. Omabacare, as a policy, is designed to do the exact opposite – lower unnecessary ER visits by providing more people with healthcare coverage. But as we are learning, when someone is habituated to receive the quick, quality care they need at a certain place (in this case, the ER), policy alone is not going to disrupt that behavioral pattern.
Leaders in the state of Utah suggested a plan to decrease ER visits. A plan that “will reward people for agreeing to stay out of the ER for non-emergency care, but also penalize them when they wind up there.” Utah lawmakers want to financially penalize people who go to the ER “unnecessarily.” But how many of us, when we need a doctor, know what is “necessary” and “unnecessary?” When you are terrified that your father has chest pain, or afraid your son may suffocate from an asthma attack, or scared because your baby has a fever….really!? Especially if you have experienced quality care in an ER before, of course it makes sense to go. And most people who are habituated to seek care at the ER are folks who were previous uninsured; people who have not paid for medical care. So suddenly they are going to pay a fine for going to get the care they needed to feel better?! What a terrible idea! As the article states, “you can’t fix ER overuse without addressing what’s causing the problem in the first place.”
I don’t know what the current status of the Utah law is, but we must take a moment to diagnose the problem before we enact a solution.
As systems engineer Dr. Peter Hovmand writes “how problems are defined has a lot to do with the solutions being sought.” Problem scoping and framing is critical to any design challenge.
When it comes to why people may unnecessarily go to the ER, here are three behavior design parameters you can use to define the problem:
1. What and how intense are the behavioral drivers to the ER?
What motivates people to go to the ER? Fear, confusion, worry? If so, they are making decisions with the emotional part of their brain.
What enables people to go to the ER? Convenience? Perhaps the ER is in close proximity. Familiarity? Maybe there is a friendly nurse who works there.
And how intense are these drivers?
2. How habituated are people to go to the ER?
How often and for how long have people been seeking care in the ER? Once per week or per month? For months or years? Or just once before?
3. What and how intense are the behavioral reinforcers to return to the ER?
What about an ER experience tells people to return when care is needed again? Perhaps the ER provides the comfort to calm the confusion. Again, how intense are these reinforcers?
Insights to these questions will help scope the problem. We must understand motivations and feelings to shed light on intervention design opportunities.
Meet Mary. She recently decided she wants to get healthy and lose some weight.
So she signs up to work with a personal trainer. Mary likes the sessions but the high cost make them difficult to maintain. She decides to get some workout DVDs and be active on her own. Her sessions quickly start to taper off. She orders a dieting book but never gets past the first few chapters. Her kids give her a Fitbit. She buys a yoga mat.
Mary has almost everything she needs to be healthy. But none of the social support, encouragement, or accountability to actually be healthy.
Then, Mary signs up for digital health coaching.
What the heck is digital health coaching?
It’s no secret that working with a health coach can be a wildly effective behavior change strategy. Research consistently shows that health coaching increases medication adherence, decreases health care costs, enhances perceived happiness, and maximizes overall health related goal achievement (₁,₂,₃,₄). But the price and logistics aren’t always an option for many people.
Enter digital health coaches.
These are real human coaches who are able to scale their services to large groups of people all over the world using digital tools. When coaching services are combined with data from wearables and apps, coaches can provide almost instant feedback on people’s health choices. This feedback is superior to the feedback a user might get from an app alone because a digital health coach is trained to translate the data on both a social and emotional level. Presenting people with large amounts of data about their behavior isn’t always enough. But presenting data in a personally meaningful context can help trigger actionable change.
Efficacy data on digital health coaching is in the early stages, but here are some important lessons from two veteran digital health coaches on how to successfully hook people in creating long term change.
1 – Break cycles of failure.
Let’s go back to Mary. She’s just started working with her digital health coach.
Every time Mary hasn’t succeeded at getting healthy in the past, instead of blaming her diet book or Fitbit or health app, she’s blamed herself. Mary doesn’t think, “These products have failed me”. She thinks, “I have failed.”
Mary’s coach can work to understand her unique history and patterns of failure to support in her in breaking through failure. How?
2 – Create a success story. Fast.
Almost immediately, Mary’s coach need to help her set a goal she can achieve. Common goals we hear from people starting a new digital health coaching program are:
- I want a 6-pack.
- I want to go to the gym five mornings each week.
Coaches can support these as long-term goals, but should quickly help people shift focus to habits and behaviors that have a high likelihood of success. Goals like:
- I will decrease my waist size by one inch.
- I will walk my dog around the block three times this week before dinner.
Immediate goal achievement is critical for self-efficacy. Self-efficacy, or a person’s belief in his/her ability to do something, builds over time with successful practice and directly determines a behavior change outcome.
Keeping people focused on the present and near future also increases likelihood of success. Rather than setting out to do 1000 sit-ups for the next year, set the goal to do 10 minutes of core exercises on Monday mornings for the next two weeks. If a person can do sit-ups for two weeks, they are more likely to do sit-ups for a month and so on. BJ Fogg’s Tiny Habits program teaches this concept of baby steps well. When she first started, Mary’s goal was to go to the gym five mornings each week. Instead, her coach worked with her to walk the dog around the neighborhood after dinner three times this week. Keeping Mary focused on completing something “smaller” in the present and near future increases her likelihood of success.
3 – Provide an enjoyable reason to believe this time will be different.
Most people, like Mary, have been slowly gaining weight pound by pound for years. They often believe – and the diet and fitness industry often tells them – that change can happen fast. But building new habits takes time. Habits are socially, emotionally, and neuroscientifically very difficult to break because they are hard-wired into the habit default center of the brain. It is easier to create a new habit than break an old habit. And to create a new habit well we must find the joy in it.
One of Mary’s new goals is to follow the Mediterranean diet.
She’s been trying the diet long before she started digital health coaching. She’ll stick with the diet for a while, but then her old habits kick in and she’ll head to the bakery for an afternoon pastry.
Instead of focusing on breaking Mary’s established pastry habit, Mary’s coach encourages her to buy some hummus and carrots for her office so she can practice eating those during her afternoon snack break. The hummus she buys is at a market she loves to visit. She soon finds that eating the hummus and carrots make her more energized and notices she’s less tempted to head to the bakery. Her coach validates and celebrates her decision every time she chooses the hummus.
Mary starts to believe that working with a digital health coach is what she needs to learn how to create habits she can maintain. This time will be different. This period of coach-led, data-driven, dynamic experimentation is critical for putting users on a path to success.
4 – Personalize for long-term engagement.
Only after coaches have established social trust do they have the opportunity to really get to know the unique lives and challenges of the people they work with. Now that Mary’s had a taste of success, she’s beginning to trust that her coach can guide her in making better decisions.
One thing we hear over and over again is “I want to know that my coach or my program knows me.” Social trust and personalization is needed for a successful coaching relationship, because
- Frustration results when people really want to do something but cannot;
- Annoyance results when something is really easy to do and people do not want to do it;
- Fear of failure is almost constantly present.
An example of the level of personalization digital coaches should strive for with the people they work with might be:
- Rather than: Have you walked for 30 minutes today?
- Instead: Hi Mary. Are you and Rover going walking along the river this afternoon?
- Rather than: What’s for dinner tonight?
- Instead: Hey Mary! What are you thinking about making for dinner tonight? I’m guessing your avocados are just about ripe by now…
Going Forward In Digital Health Coaching
This year we’ve seen a surge of business announcements related to digital health coaching: In February, MyFitnessPal announced it’s acquisition of Sessions. In April, Omada Health completed a Series B funding round of 23 million dollars. In May, Weight Watchers acquired online fitness startup Wello. In July, Kurbo Health announced that it raised $5.8 million to “use digital health coaches to help fight childhood obesity.”
Each of these companies leverage the power of technology to strengthen and scale human-to-human coaching relationships to make big impacts on people’s journey towards better health.
It’s not all about the technology, though. It’s about leveraging the power of technology to strengthen and scale the human-to-human relationships that can hook people better than an app ever could. Digital health coaches are the human force behind people’s journey to better, sustainable health.
A brilliant research colleague of mine recently posted the following questions about U.S. health insurance companies:
- How do these insurance companies work?
- How does money move through the organization?
- If we could follow the dollar …where does it go? Do they invest it? Cost is going up, but these companies are still making much money. How do they do that?
- Who makes the decisions inside the organizations? What are their decision making processes?
I love this topic, and more and more about the economics of health care in this first year of Obamacare is unfolding every day. To understand how the money flows, let’s glance at a chronology of health insurance industry money.
Pricing. There are two basic types of health insurance pricing:
- Community pricing
- Actuarial pricing
Community pricing: economists look at a whole community to figure out what it costs to take care of that community. They then divide that total dollar amount by the number of people in the community. The resulting figure becomes the price per person for health insurance premiums.
Community pricing was the standard practice after World War II in the U.S. Back then the government left health insurance companies to run tax free; and it was a time when the average cost of insurance company administration was 5% leaving 95% of insurance premium dollars to be dispensed on health care. Back then premium prices were low – the sick and the healthy paid the same amount, but that didn’t bother anyone. Then, in the 1970s we saw an explosion of a) demographics and b) medical technology. People started living longer. Specifically, in the year 2000 as many people over the age of 100 were alive as were people over the age of 65 in 1965. The longer people live, the more likely they will need health care. But back in 1965, there were no CT scans nor super expensive medications. So booming demographics and expensive technology, along with c) laws that demand that more and more health conditions must be covered by insurance, drove health insurance premium prices sky high.
Actuarial pricing: economists look at an individual person to determine the risk of getting sick to price insurance premiums. In this case, healthy people have low premiums and sick people have higher premiums.
Obamacare is a hybrid of community and actuarial pricing. The theory is that if enough young, healthy people buy health insurance they do not use, it will offset the costs of paying for chronically sick people. We don’t know yet if this “economics of balance” will work ……
But back to actuarial pricing -> insurance companies figured out how to weed out the sick people by denying care according to pre-existing conditions. Denial of care practices evolved 1975 – 1992 as health care costs began to explode. Large scale HMOs became the popular practice by the early 1990s when Wall Street entered health care to financialize it as they did so by focusing on management practices. During this evolution, management/administrative expenses crept up from 5% to 35% of health care operating budgets. One year the head of United Healthcare was paid a salary of $1.2 billion dollars! Finally, the government intervened by passing a law that the maximum amount of health care premium dollars allowed to be spent on administration was 20%. Since then, regardless of how health care is priced, we have the 80/20 rule mandating that 80% (and in some cases 85%) of premium dollars must be spent on providing care.
Gaming the System. It was not just health insurance exectives that practiced greed & cheat during this time. Providers (hopsitals, doctors) responded to the management of health care by gaming the system: for instance, hospital administrators would tell doctors to discharge patients as soon as possible, and often before they were ready. Because the less time a patient spent in the hospital, the more profit for that hospital. And if a patient needed a CT scan during their stay, for instance, doctors were ordered to discharge the patient and re-order the test as an outpatient procedure. While doctors may have felt uncomfortable, they did what they were told because 1) they were rated by hospital administrators and 2) they figured the patient who left too early would come back, allowing the hospital to collect money for two admissions instead of one. Another popular trick for gaming the system was via hospital codes and prices: wildly inflated hospital prices, that in many places are still prevalent today.
How Insurance Companies Make Money. There are two basic ways health insurance companies make money:
When premium money comes into a health insurance company, it is referred to as a “loss” to set a mindset to avoid dispensing care. Health insurance companies try to minimize losses by setting up doctor panels and negotiating in-network contracts, for instance….practices that determine how much money a health insurance company pays providers for care (reimbursement). One such practice to determine health care reimbursement is called a Diagnosis Related Group (DRG). So let’s say it is determined that the cost of someone having a heart attack is $X. The health insurance company says to the hospital “We are going to pay you $X for a heart attack. Many heart attack patients who come in will experience complications that cost you more, and many other patients will experience easy recoveries that will cost you less, so it’s your job to decide how to manage the patients and practice medicine so you don’t go broke.” For this reason, doctors have been pressured to practice the cheapest medicine possible to maximize hospital profits by seeing as many patients per day as possible – a practice called fee-for-service. But Obamacare is forcing providers and insurance companies like Blue Cross away from fee-for-service health care. And Aetna recently published information on health insurance premium rates to inform customers how they are using their money.
Again, 80% of the money within a health insurance company must be spent on care. If there is ever any money left over from that 80%, health insurance companies must give back to customers via premium rebate checks.
In terms of investments, many health insurance companies are putting their money into digital health. A recent RockHealth funding report showed 2014 digital health funding activity to date. Whether or not these investments will be profitable is yet to be determined.
Moving Forward with Obamacare. Until Obamacare, individuals could only qualify for health insurance through employer work benefits, Medicaid (if financially qualified), or Medicare (if over the age of 65). This left millions of Americans without health insurance, creating a chaotic and uncontrollable economic landscape. Obamacare is rapidly spiking the number of people with health insurance, hypothetically balancing the economics. We don’t know yet. Like one insurance adjuster said to me “Pricing Obamacare health care plans is a bit like throwing darts: we don’t know yet how to make a profit until the many unknown accounting variables (are the newly insured paying their monthly premiums, how many are using state exchange plans vs. Medicaid, how many providers are accepting Obamacare contracts, etc.) settle in.” My guess is that at the close of 2014, once we have the chance to analyze Obamacare Year 1, insurance companies will come out to say they need to elevate premiums, while consumers and larger companies will demand more affordable health care.
The Wall Street Journal recently published a story that ER visits are on the rise despite rollout of the Affordable Care Act.
Lawmakers wanted “to give the uninsured better access to primary-care doctors who could treat routine ailments and prevent chronic disease, with the intent of keeping patients out of the ER and lowering the cost of care”….but “Instead, the ER doctor group’s research and several other recent studies suggest that people who gain private and government insurance are more likely to seek emergency care.”
Of course we can point to a lack of sufficient primary care providers – we do not have enough here in the U.S. to handle all the newly insured. However, here’s a different perspective on why people are still seeking care in the ER:
Most uninsured people go to the ER when they need medical care. This is their habit. Because for years in this country, without health insurance, you could still seek medical attention in the ER. A habit is very difficult to break. In fact, an established habit is harder to break than forming a new habit is to make.
When you do a new behavior – let’s say, cook a meal you have never cooked before – your brain works hard to think about how to do it…how to follow the recipe, what temperate to set the stove at, etc. All that thinking takes place in the front of your brain, the prefrontal cortex. Keep in mind, the human brain is 1 – 1.5% of a human body weight, but takes up to 25% of the energy a human body produces to work. So any time you do something new, the brain function related to that new behavior consumes a lot of energy in the prefrontal cortex.
Once that behavior becomes familiar – meaning, you can cook that meal so easily you don’t need to think about the details – that thinking moves into the habit center of the brain, the striatum. Cooking that meal then becomes your habit. Habits, or defaults behaviors, are strongly wired in the brain and require little energy to maintain. This is why eating behaviors are so hard to change.
As we are learning, giving health insurance to people who are in the habit of seeking care in the ER will not be enough to break their habit. Going to the ER is their default behavior. I would bet that instead, they figure they can continue going to the ER and be covered. Their habit will now be paid for. These folks will not learn a new habit of going elsewhere until we design obvious and easy pathways for them to develop a new habit around seeking care.
Learning to go to a primary care provider is a new behavior for the uninsured who are now insured. Which means as health care designers, we need to figure out how to effectively tap into the prefrontal cortex of millions of people. I’d say we have important work to do!