Mayo Clinic and Sensely are collaborating on solutions that allow people to receive trusted health guidance and information, leading to more appropriate diagnosis and treatments. Sensely’s proven, empathy-first patient engagement platform leverages Mayo Clinic symptom triage algorithms to deliver care advice. Using both voice and chat functions, the Sensely virtual nurse "Molly" asks symptom-related questions, and through branching logic provides personalized health information and care recommendations such as to see a provider, go to the emergency room, or manage symptoms at home. In this presentation by Sarah Vande Loo, audience members will learn how Mayo Clinic symptom triage algorithms are structured and delivered to the Sensely app; how content is adapted for voice and chat features, and challenges and lessons learned in developing content for voice and chat interfaces. November 30, 2017

2. Nurse Avatar Delivers Mayo Clinic Symptom
Care Advice
Sarah Vande Loo
Information Development World
November 30, 2017
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3. Sarah Vande Loo
Senior Knowledge Architect
Global Business Solutions, Mayo Clinic
• 15+ years working with digital content
• Leading a Decision Support team in
building algorithms and authoring
solutions for flexible delivery models to
create once and publish to multiple
platforms
• Advancing the use of decision tree and
algorithm-based technology
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4. Mayo Clinic Global Business Solutions
PURPOSE: To extend Mayo Clinic’s knowledge and capabilities by
delivering integrated, market-leading health and healthy living solutions to
patients and consumers, and to create and enhance meaningful
relationships with Mayo Clinic at all stages of life.
©2017 MFMER

5. Agenda
• Overview of the Mayo Clinic symptom triage algorithms
• How the symptom triage algorithms are structured and delivered to
the Sensely app
• How content is adapted for voice and chat features
• Challenges and lessons learned in developing content for voice and
chat interfaces
©2017 MFMER

6. Meet Molly
• Molly is a nurse avatar developed by
Sensely ― an empathy-first patient
engagement platform
• Molly leverages Mayo Clinic symptom
triage algorithms to deliver care advice
through voice and chat functions
©2017 MFMER

7. Symptom Triage Algorithms
• Developed 10+ years ago by Mayo Clinic physicians and nurses, and
used in primary care practice sites
o Self-service triage tool (Ask Mayo Clinic Online) uses a subset
of algorithms
• Personalized content and questions based on user-reported
data inputs
• Structured assets to allow for flexible use and multi-channel delivery
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8. Authoring Content and Algorithms
• Allows SMEs to build machine-executable logic or pathways
(algorithms) for knowledge exchange and content delivery
• Provides a central database for reusable and shareable assets
o Example: The question “Have the symptoms been getting significantly
worse?” can be reused in multiple algorithms
• Uses logic to dynamically deliver contextualized content to users
(right content to the right person at the right time)
• Language variations coded directly in the content switch between
[Do you/Does he/Does she] based on data inputs at runtime;
Add or remove text strings within assets based on age or gender
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9. Content Structure
QUESTION ANSWERS
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10. Content Structure
RECOMMENDATION SELF-CARE
SUBPOINTS
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11. Build an Algorithm
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12. Big Algorithms!
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13. Questions
Results
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14. Results
Questions
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15. Supporting Voice and Chat
• Ran content reports of assets and fields for review
• Developed voice variations of the questions for Molly to speak:
o Adapting multiple sentences into one, call to action at the beginning vs.
end of the phrasing
• Voice recognition for user responses
o Recognizing user speech variants and synonyms (bug bite vs. insect
bite) to select symptoms and answer questions
• Map voice asset variants to core symptom assets through unique
identifiers
• Delivery of content and algorithms via a stateless API – continuing to hold
the source content for ease in updates and maintenance
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16. Supporting Voice and Chat
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Voice
Voice questions
(what Molly speaks)
Display question (summary)
Answer list
Voice recognition to
select answers
Chat
Voice questions
(display only)
Answer list

17. ©2017 MFMER
Voice Chat

18. Challenges & Lessons Learned
• Adapting content for voice – new skillset for editors and writers
o Lunch-and-learns and other educational opportunities for staff
• Addressing disparate needs of various user groups (clinical
practice users vs. patients/consumers)
o Multiple asset fields for various uses, stored and supported in a
single location
• Maintaining voice content in secondary source
o Bring into a single source to support all delivery channels and
content types (e.g., Alexa first aid skill and symptom triage)
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19. Challenges & Lessons Learned
• Accounting for natural language variations in the voice content (e.g.,
adjustments for gender, perspective – do you/does he)
o Narrow focus for first phase (e.g., set all as second person ‘you/your’
and adults only)
• Adapting questions for voice interactions (yes/no and single choice
vs. multiple choice or value entry)
o Simpler questions and answers better for voice apps
o Potential to move into AI-based interactions: leveraging historical data,
outcomes and calculating probability on combinations of symptoms
leading to certain endpoints vs. prescriptive pathways
©2017 MFMER

20. Questions?