In 2018 the Mayo Clinic came to
where I was the NYC Design Director,
to learn to innovate.
They wanted to learn and ship quickly and solve real
problems with software. Myself and
thoughtbot NYCs Managing Director, established an engagement
with the Mayo Clinic focused on
building an innovation practice.
We would develop the innovation practice through building a product. That
product would be OnPar; an educational game to improve the continuing
education methods for doctors, nurses and residents. This is the story of
how we built the product and helped the Mayo Clinic build an
In our discovery phase with Mayo, we uncovered a problem:
current continuing education methods for doctors, nurses and
residents were not engaging, were expensive to produce, and mildly
effective. With some assumptions, I put together a
customer development interview (based off the template below),
and set off to interview ~25 individuals in, and tangental to, what
we thought our target market was: health care professionals.
The continuing educational space had competition. There was qualitative
research that supported our assumptions around engagement, expense, and
effectiveness. We aligned that this was a good starting point for our
project. As the project, lead I took on many responsibilities:
Lead and execute customer development and research.
Facilitate solution exploration and testing.
Lead interface and experience design.
Establish experiment process, setup and analysis.
Develop the product roadmap and manage the project.
Communicate with, align, and present to stakeholders.
Build, manage, and mentor the design team.
As with most successful projects, it takes a village. I was
lucky to be surrounded with amazing thoughtbot designers and
engineers, and a Mayo Clinic administrative duo.
The Mayo Clinic also provided us with world
class Doctors to create the case content, as well
as a board to lobby for investment.
Mark Warner, MD
Barbara Baasch Thomas
Richer Berger, MD
David Cook, MD
Jane Linderbaum, NP
Rozalina McCoy, MD
Farrell Lloyd, MD
Jeannie Poterucha Carter
For this project I chose a lean product approach. We would
discover a problem, identify the business, develop the customer, identify
risks and assumptions, then build measure and learn from the solutions
Current continuing education methods for doctors, nurses and residents
are not engaging, expensive to produce, and only somewhat effective.
Create an engaging method of continuing education.
Improve effectiveness of continuing education.
Reduce the cost of creating content.
Have a proven viable business model.
We did a competitive audit to understand what we were up against, and
potentially the size of our market. The audit validated our problem space,
gave us a sense of market size, and illuminated our
unique value proposition: world class doctors creating content.
With an understanding of our problem, our goals, and our competition,
we kicked off a design sprint to test our assumptions. The Google Venture
Design Sprint emphasizes effective problem identification and iterative
testing. It accelerates learning and provides clear direction.
For our first sprint, we tested on a card game as a solution. We
incorporated elements we'd heard from our target customers about what might
engage them: solving real cases, pitting doctors against an expert
clinician, and professionalism. The initial prototype was a physical card
game made using index cards.
We set out to test the card game with 15 doctors in the Mayo Clinic. We
ended up testing it with around 40. Doctors were not only eager to play, but
they were finding other doctors in the clinic to share their experience
with, and recommend they play. We had real world referrals to a prototype.
Aside for the engaement, the game answered many of our assumptions and
helped us understand short term needs and a potential long term direction.
Our next task
was to create a simple digital version of the game and get it out to our
target customers to get more feedback. Our team had proficient
with interaction, so we decided we'd build a web based app..
We prioritized a mobile platform as continuing education
for doctors was generally done "between things". Often times on a commute,
or between appointments. But rarely was this work done at home. When
doctors got home, they were tired and wanted to rest.
Here is the case picking screen. You'll notice the first version is quite
bare bones, but offers a few "nice-to-have" features our test groups asked
for; the last score a doctor got was displayed, and unplayed cases were
By version 4, we'd made many changes to the case picking screen.
The words “real case” meant a lot to the users, so we
emphasizing it. By doing so we saw an increase in the number
of cases started. However, it also
caused a a small reduction in retention. It turns the users now had
concerns about PHI (Protected Health Information). We later
addressed this on the "meet the patient" screen.
Another change was using the Mayo Logo to improve trust.
Adding it showed an increase in cases started and retention.
Finally, we highlighted "par" (the score to beat) for each case.
Doctors told us they were very competitive, and emphasizing the score
signaled what to beat. We saw an increase in cases started with
lower par numbers, and a reduction of cases started with higher par numbers.
Another important aspect of the game was being introduced to the patient. We
methodology and maintined that language. On the screen there is an image of
a patient, and some basic information about that patient. From here the user
goes on to ask questions to this patient.
The initial version uncovered some significant learnings; people were
concerned the case might contain PHI (Protected Health Information), images
of people were not helpful unless providing diagnostic clues, and finally
proper domain language was a must. In our iterations, we addressed these
issues in several ways. We highlighted the fact that we removed any PHE. We
removed all of the images of people and only used relevant information. And
finally, we updated all of our language to be domain language
After meeting the patient, we were on to game play.
Asking a question (playing a card) added 1 to your score.
You'd ask a question by dragging a card to the playing field (middle).
Once a question was asked, the card would flip, and the answer exposed.
You could freely page through all the questions, and diagnose
the patient whenever you wanted..
Through interviews and data we learned a lot, and made adjustments. Doctors
were not happy that all questions had the same cost to play - they felt some
questions or test should cost more. Adding variable costs to cards increased
retention. Our original game mechanics were janky; it wasn't obvious where
to drop the card, once you'd asked 4 questions it was nearly impossible to
scroll up, and not being able to easily revisit the patient information
provided uneccesary friction.
The climax of the game was the diagnosis screen.
Here was the chance for the user to make their diagnosis and
solve the case.
The original screen was quite simple,
the user could see par, review the questions they asked,
and make a diagnosis. Once they made the diagnosis, they were
either correct and celebrated, or wrong and shown the correct answer.
During our in-person interviews there were a lot of “I knew its” and “oh no,
it can’t be that” on this screen. There was a lot of emotion from the
users. The question most often asked when the diagnosis was wrong was
"how did the expert do it?". They were curious what questions would
have been a better line of thought.
The biggest win we had in the product was adding adding a section
that showed the line of questioning an expert took to solve it.
By adding this, we say a significant increase in cases played and
Ultimately the iterations proved successful as we saw
steady engagement, an upwards trend in acquisition,
good 30 day retention (adding new cases was the only driver of
continued retention), and signal of customers willing to pay.
After extensive testing and pivoting, we discovered a successful business
model for OnPar. Our shift from targeting individual users to focusing on
those invested in learning and credentialing led us to two types of paying
Firstly, nurse organizations desired our platform for ongoing education,
curated content dissemination, and tracking participant engagement.
Secondly, larger organizations like the CDC saw the value in leveraging our
platform for urgent messaging, improved communication, and more accurate
tracking compared to their existing systems.
These distinct customer perspectives guided our final pivot, resulting in a
problem-solution fit and a group of paying customers. Moreover, the Mayo
Clinic team gained valuable insights in product development,
decision-making, customer engagement, iteration, and innovation. This
successful product launch marked the establishment of the Mayo Clinic's
digital innovation lab.
The biggest challenge with this project was changing the mindset of the Mayo
Clinic from shipping something perfect, to shipping something to learn. The
two things we struggle with was shipping something scrappy under the Mayo
brand, and keeping the board aligned with learning as progress. We focused
on metrics often to show progress, but struggled with expressing how
important the qualitative learnings were, and that a failed version of an
A/B test was a path forward, not a set back. This took a lot of hand
holding, weekly meetings, and education. I leveraged the
Stop, Pivot, or Preserve framework
framework to disclose learings and progress, as well as force
alignment and decision making as a team.
Our key realization was that we initially targeted the wrong paying customer
for the project. While doctors and residents found value in the product,
they were reluctant to pay for it. Nurses were willing to pay, but we
couldn't sustain their engagement beyond 60 days. Ultimately, department
heads emerged as the paying customer, benefiting from the product's insights
into residents and nurses, enabling them to address knowledge gaps and track
performance in their organizations.
The collaboration between thoughtbot and the Mayo Clinic resulted in OnPar,
an innovative educational game for healthcare professionals. Through a lean
product approach and iterative design, the team addressed the shortcomings
of traditional continuing education methods. OnPar successfully launched,
attracting paying customers such as nurse organizations and larger
institutions like the CDC. project established the Mayo Clinic's digital
innovation lab and provided valuable insights into product development,
customer engagement, and innovation in healthcare. OnPar represents a
cultural shift in the Mayo Clinic.