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Nurse Avatar Delivers Mayo Clinic Symptom
Care Advice
Sarah Vande Loo
Information Development World
November 30, 2017
©2017 MFMER
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
©2017 MFMER
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
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
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
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
©2017 MFMER
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
©2017 MFMER
Content Structure
QUESTION ANSWERS
©2017 MFMER
Content Structure
RECOMMENDATION SELF-CARE
SUBPOINTS
©2017 MFMER
Build an Algorithm
©2017 MFMER
Big Algorithms!
©2017 MFMER
Questions
Results
©2017 MFMER
Results
Questions
©2017 MFMER
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
©2017 MFMER
Supporting Voice and Chat
©2017 MFMER
Voice
Voice questions
(what Molly speaks)
Display question (summary)
Answer list
Voice recognition to
select answers
Chat
Voice questions
(display only)
Answer list
©2017 MFMER
Voice Chat
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)
©2017 MFMER
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
Questions?

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Nurse Avatar Delivers Mayo Clinic Symptom Care Advice with Sarah Vande Loo

  • 1.
  • 2. Nurse Avatar Delivers Mayo Clinic Symptom Care Advice Sarah Vande Loo Information Development World November 30, 2017 ©2017 MFMER
  • 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 ©2017 MFMER
  • 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 ©2017 MFMER
  • 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 ©2017 MFMER
  • 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 ©2017 MFMER
  • 16. Supporting Voice and Chat ©2017 MFMER Voice Voice questions (what Molly speaks) Display question (summary) Answer list Voice recognition to select answers Chat Voice questions (display only) Answer list
  • 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) ©2017 MFMER
  • 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