Treatment library test
Introducing our treatment library by capturing patients’ preferences.
BACKGROUND
After our care-forward onboarding launch, another key question that we saw patients frequently ask us about was the type of treatments we offer. Moving away from a product-forward onboarding experience also meant providing less specific information about treatments, since we focused our messaging around overall quality of care.
Because we saw such a marked interest in the treatments we offer, I decided to put together an A/B test where we introduced patients to our treatment library. Previously onboarding tests taught us that whatever information we surfaced to patients has to be personalized to the responses they give us throughout onboarding.
Instead of focusing on telling patients about our treatments, I designed a new approach where we instead collected information about the types of treatments patients are or are not comfortable with and surface our treatment library as a response
Role
Senior end-to-end product designer working alongside another senior designer.
Challenge
Introducing our patients to our treatment library by capturing their treatment preferences.
Results
5.94% conversion rate for patients who saw these screens vs. 4.82% conversion rate no screens.
HYPOTHESIS & GOALS
To frame the purpose of this test, we gathered baseline data and formulated a hypothesis on which to base our results.
Problem
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Overall conversion sat at around 2%.
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Lots of patients dropping off because they ‘need more info’ and adding comments like ‘What is the prescription medication?’, ‘I know nothing about this product’, ‘I can't exercise my knees’, and ‘What u mean 1 non-opioid in our payment drop-off survey.
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In UT, testers frequently say that in order to give us a try, they’d need more information about the types of treatments we offer.
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Generally, our audience tends to have had negative experiences with the healthcare system in that they 1) feel like they’ve already tried everything and 2) have been pushed into treatments they don’t want. We needed to find a way to build trust in this respect.
Hypothesis
We believe that by
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adding a moment where we captured information about patient’s expectations about their treatment and then
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surfacing our treatment library as a response
we could build trust and decrease drop-off at payment because we are surfacing information about our treatments in response to patient inputs.
Goal
Lower drop-off rates at payment leading to higher conversion rates
DESIGN PROCESS
I started by laying out the type of questions that patients may have about our treatments, and also what questions we may have about our patients. I met with internal stakeholders to speak through the type of treatments we wanted to introduce from our library, and at a high-level determine how specifically we wanted to talk about treatments.
In conversations with Dr. Jacob, we also started talking about the types of treatment categories that exist for pain, from most to least invasive, natural to medical, over-the-counter to prescription. We gathered data around the types of treatments people were already telling us they are trying and uncovered that most have tried a combination of things from different categories.
I also gathered inspiration as to what other health-tech companies are currently doing to introduce the notion of their treatments. Generally, I found that there is no one approach that companies are taking and that there was a lot of room for improvement in existing experiences.
Conceptually, I wanted to focus on collecting information about patients' treatment preferences. I started wireframing different ideas that introduced a prompt and simple yes/no responses to enable patients to tell us what they are and aren’t comfortable with. Then, I started exploring how to best position our treatment library as a response to patient responses.
I user-tested a few rapid prototypes that approached prompts in slightly different ways and continued exploring potential UI for presenting our treatment library in a way that best reflects back patient responses. Through this process, we learned that this approach was effective in helping patients feel listened to while also communicating the types of treatment available at Clearing. The entire design and research process took about 2 weeks, with several weeks of the build before launching the A/B test.
SOLUTION
We launched the treatment library A/B test and measured performance. Interestingly, by the time that we launched this A/B test, we were pivoting our business model in order to onboard patients with insurance. Therefore, the results of the test would help determine if this prompt framework was something we should expand on or nix for the upcoming onboarding redesign pt. 2
RESULTS
We ran the A/B test for about a month before reviewing the results. We found that generally, patients that interacted with the treatment prompt and library were more likely to convert than patients that didn’t. We saw all types of behavior, from patients telling us they are interested in all treatments to those telling us they were only interested in a few, to some saying they weren’t interested in any of them.
We also saw a lot of patients interact with the treatment library summary at the end, de-selecting treatments they had originally said yes to, and other selecting treatments they had originally said no to. It was a really interesting test that ultimately led us to expand this concept to ask about other preferences, like the type of care patients want to receive from a provider.
Now that we’re working on redesigning our onboarding to accommodate patients that have insurance, we have expanded this framework to continue evangelizing our patient-led care-forward model.