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Forget about Features, Design Behaviors

Note: I wrote this piece for the launch of Prescribe Design, then it was republished in MedCity News. 

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:

  1. 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.
  1. 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.

  1. 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.
  1. 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.
  1. 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.

 

ER visits: Diagnose the Problem before you Design the Solution

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.

The “Modify” Factor: designing engagement

If you have ever taken a yoga class, you know some poses are harder than others. Some poses you can do, some poses you can’t do, and some you can kind of, sort of, do. What determines whether or not a pose is “hard” for someone ranges from experience to motivation to social norms to knowledge to mood to physical capacity to energy.

When a pose is “too hard” any good yoga teacher will encourage a student to “modify” the pose. To modify a pose means to physically adjust your body so the pose is easier, and/or less painful, and/or, more enjoyable, and/or feels better. The lesson in modify is to discover a slightly new and different way to move your body so it works best for you. Typically the decision to modify happens in real time, in that moment of the practice.

Modifying is vital for a quality yoga experience for many reasons, including but not limited to:

  • decreasing pain
  • increasing positive feelings and fun
  • increasing ability and self-efficacy

and overall, empowering the student to practice ways to stay engaged in the class.

Otherwise a student might have a negative experience, feel like they are “not good enough”, end up in pain, and/or never do yoga again.

What digital health products are not doing well yet is designing for the user’s need to modify. At some point, we all need to modify. Whether we are beginner or advanced, calm or stressed, motivated or lazy; sometimes we just need to modify. Being able to modify – because our teacher tells us to or because we decide we need to – in real time is a critical factor in sustained engagement.

So how can digital health products better design for the “modify” factor?

Designing around real human interaction or customer service is key for a healthcare engagement experience.

Some start-up digital healthcare companies  – like Sessions, PokitDok, Sherpaa, Omada Health, Better, Hula (originally Qpid.me) and Atelion Health, Inc (originally CollaborRhythm) – seem to be trying. Sessions, for instance, provides exercise health coaches with whom users can interact via text, e-mail, and phone when needed throughout a 12-week program. When a Sessions user first signs up, s/he links with a health coach, who calls to conduct an in-depth starter session. During this initial phone experience, the coach asks a basic set of critical questions to assess where the user is at. Once the program is underway, the coach regularly interacts with the user and vice versa. Sessions Founder Nick Crocker wrote, “people are adding a human layer on top of these [technology] applications, putting the power not just in the hands of the consumer, but in the hands of their network. This provides an incredible resource to doctors, trainers, and others who help people achieve their health goals.” I would argue that the technology is the layer on top of the human interaction, shifting some of the resource burden from the health provider to the user. Which is a good thing.

Authority

The main value of designing your technology around a human “authority”(a coach or some sort of figure who the user trusts) is that your solution will “meet the user where s/he is at” when they need to modify.

Sherpaa is a group of NYC based doctors and specialty providers who answer your most pressing healthcare questions via phone or app.  When a user enlists with Sherpaa, they are able to navigate the healthcare system with the guidance of an expert when they need it. Informed decisions made in real time. As you need to modify. As it says on their website, “That’s what we’re here for.”

Philip’s DirectLife uses human coaches to assess which messages get a particular user to eat more healthfully and exercise more consistently. What we are learning is that it is not the same to hear an automated coach saying “You’re doing a great job! I know you like positive feedback so that’s why I’m giving it to you.” The messages must be contextually relevant and personally meaningful in real time. Part of “meaningful” is a belief that the message is coming from a place of perceived authority.

Livestrong.org offers patients “navigation services to provide the support you need as you face your cancer journey.” That happens through individual mentors who have deep experience with various aspects of cancer treatment. Like one Livestrong user told me “When I found out I had cancer, I went to Livestrong and immediately logged in, and gave them all my details via an online health questionnaire. An oncology nurse called me 24-hours later…and depending on how many boxes I checked, I could be linked to as many “helpers” as I wanted e.g. a Financial, Mentor, Clinical Trials expert, etc. I got a call from someone responsible for matching me to a clinical trial. He gave me info for all the clinical trials around the U.S. relevant to my cancer. I didn’t use clinical trial guy b/c I didn’t want to lose my control over my treatment decisions. I wanted to choose my chemo drug and augment my services as needed as I went. But the oncology nurse was invaluable – she was half shrink/ half nurse. She provided the list of questions to ask my doctor. She knew….She helped me flush out questions and prioritize.”

Social Trust

If you cannot integrate a human authority into your solution, consider building in a social network. A social network solves for the human need because by design, it is person to person. You know there are other real people on the other end of the interaction.  If I knew that when I posted to Facebook, the other people reading were my trusted, valued healthcare providers, I’d engage to share health information because I would believe responses to my post would help me figure out how to modify my health.  This is one reason why patient portals – or Online Health Communities (OHCs) – help users make more empowered decisions and stay engaged in health. Because people who use OHCs trust the other members as authorities and have the chance to practice modifications.

The largest patient portal in the world is PatientsLikeMe (PLM). Approximately 230,000 patients engage with PLM. According to co-founder Jamie Heywood, over 2,000 health conditions are mentioned; 4,000 posts; and 16 million data points are logged per year.[1]

I listened to Ben Heywood, also co-founder of PatientsLikeMe, at the recent Connected Health Symposium in Boston, and he said one big trend they are seeing is users better adhering to their treatments and better remaining engaged in their care. PLM published research in the Journal of Epilepsy that shows how PLM engagement increased adherence tied to outcomes by 19% among patients with epilepsy. “Prior to using the site, a third of respondents did not know anyone else with epilepsy with whom they could talk; of these, 63% now had at least one other patient with whom they could connect. Perceived benefits include: finding another patient experiencing the same symptoms, gaining a better understanding of seizures, and learning more about symptoms and treatments[2] .”  Users of PLM trust other users.

SmartPatients, a start-up patient portal specifically for the cancer community, is also seeing an increase in adherence to treatments. SmartPatients co-founder Dr. Roni Zeiger, during Health 2.0 Demo Day, said “this portal is increasing adherence to treatments due to social support. People are showing up to treatments even though they don’t want to because of their portal peer advice and encouragement.” Point: a patient who intended not to go to treatment modified that decision and instead went.

According to a recent U.S. Healthcare IT report, the U.S. patient portal market is expected to reach $898.4 million by 2017, up from $279.8 million last year — a 221% increase. Nearly 50% of hospitals and 40% of ambulatory practices currently possess patient portal technology. How well those portals are designed for engagement is yet to be seen.

Like one patient recently told me, “I get on message boards to type in questions and it directs me to where people write about the answers. Some of it pertains to me, most of it doesn’t. It’s just some people talking, though I am not sure who these people are. I find out when I read responses on those discussion boards, I have to sift through so much riff raff. Then I wonder what to trust. In the end, I called ahead to the radiology department at the hospital where I am going for my procedure and he explained for me every step of the experience I am going to have. I had an expert tell me what I wanted to know. That helped.”

What this is all about – and what is needed when you want to modify – is trust. Trust that your modification will make it better. So if you are going to build a social network into your product, make sure it allows for trustworthy interactions:             

According to this study, The strongest finding was that “maintaining a highly cohesive network is necessary for building trusting relationships in OHCs” and that portal designers should design so “members easily recognize and reach others whom they can trust…..such as designing and installing member mutual rating systems (for members’ contribution, caring for other members, and integrity)” 

Even better, though, enable a meaningful 1-1 interactions. Build digital health technology that is an extension of what is already working in real life.  I recently interviewed a cancer patient who was first diagnosed in 2007 and then again in 2012, and he said “The conversation where I received the most support during my treatment was right before my first stem cell transplant – I got a call from a friend who had been through it, and she told me what to expect. She talked me through the process and made herself available to me when I had questions. It helped with my decisions. It was so comforting.” He might have been able to have a similar experience with Better – because Better enables users to tap into the massive database of the Mayo Clinic for immediate health care information. It’s not just reliable information, though, it’s on demand assistance. Users can call Mayo Clinic nurses to talk about the information and any discuss questions or concerns they may have. On the spot.

To clarify, designing for the modify factor is not about getting users to your product or program for the first time – it is not about persuading a first time yoga student to enter the yoga studio. The modify factor is about designing the engagement experience once the students is there. Keeping your user engaged once they have arrived.

What you can do:

Conduct user research so you are clear about your target customer’s needs and values. This will not only allow you to empathize and capture user behaviors, but also allow you to know what is needed to build trust. Health is social; we want other people to validate our decisions.

Prototype often so you can test how well your solution is meeting the needs of your users. Too few healthcare companies do this. Health happens in real time – we need what we need when we need it. An ongoing prototyping plans enables you to build agility into your solution.

Define clearly what engagement means to your business and integrate a way to measure that engagement over time so you can regularly pinpoint “the modify factor.”

 *Thanks to @Nir Eyal for pre-reading this post.

Good Healthcare

I spoke on a panel yesterday at the MobileBeat2013 conference. The conversation focused on leveraging behavior design for good digital products and services. When the moderator, Nir Eyal, asked for examples of companies that are getting it right, here are a few (+ more) of the examples I mentioned:

Sessions – Users experience personalized digital health coaching  via SMS and e-mail. Content is dynamic to match motivation levels and grounded in proven behavioral science strategies.  On the other end of that digital interaction is a live, human health coach.

PokitDok – Users experience customized healthcare consumerism via web and mobile app. Search options allow users to match themselves to a healthcare provider according to their desired specialty, location, and price.  On the other end of that transaction is a live, human healthcare provider.

Sherpaa – Users experience personalized health advice via phone and email. Employees can easily access doctors with questions around health and health insurance when needed. On the other end of that query is a live, human medical doctor.

Notice the trends?

Reference Based Pricing

Below is a raw chronology of how health insurance companies have arrived at reference-based pricing (RBP) or “maximum allowable amount” or “max price.” RBP is the new “in-network.” Just one of several trends emerging in this new age of healthcare consumerism in America. It will impact our out of pocket (OOP) costs.

  • 15 years ago when the Clinton administration introduced HMO models, independent/private practice doctors negotiated a price-per-patient [“capitation”] deal with health insurance companies. Which means the insurance companies went to docs to say “we have 1 million patients in our network, we will send them all to you for $X/per patient” and the docs said “okay” and so “in-network” was born. A patient was allowed to go only to that provider that the insurance co. had negotiated with. It was an exclusive deal. And doctors who were good at negotiating/business, made a lot of money.
  • Over time, doctors got angry, because most doctors are not good at negotiating, so insurance companies were making loads of money, and doctors were losing; plus, these contracts were fixed prices over 5/10/15 years, so they did not account for rise in operating/living costs. So private practice/independent doctors banded together to argue against insurance companies. In response, health insurance companies sued the doctors under anti-trust laws. And the insurance companies won, because doctors collaborated with each other even though they were not in the same company – they colluded to negotiate with the insurance companies – which is considered price-fixing. However, when doctors are a part of the same company (like Mayo Clinic), then it’s not price fixing. So doctors formed a new entity in medicine, known as multi-specialty groups. The only examples of multi-specialty groups at the time included for instance, Mayo Clinic and Kaiser Permanente. (more…)