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AI in Action: Elevating Patient Insights
1. AI in Action: Elevating Patient
Insights for Superior Outcomes
Emily Lewis, MS, CCRP
AI and Innovation Lead, Neurology
UCB Biopharma
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UCB is a global
biopharmaceutical
company focused on
solutions for people
living with severe
diseases.
By putting people at the
heart of everything we do,
we enable people to live
their best lives, as free
as possible from the
challenges of disease.
From Solution
to Patient
We strive for a unique
patient experience,
providing solutions
with the highest
possible impact.
From Patient
to Science
We pursue a deep
understanding of patient
sub-populations to
develop an original
scientific hypothesis.
From Science to Solution
We aim to translate scientific hypotheses into
innovative solutions and engage patients in the journey.
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UCB’s patient value ambition
UCB Team Name [XXXXXXX] - UCB - Approval [XX-XX-XXX] - Approval date [XX Month XXXX] ! GO TO INSERT>HEADER FOOTER to change.
Creating value for patients now and into the future
Unique outcomes that
help specific patients
achieve their goals
Best individual
experience
Ensuring access to all
those who need these
solutions in a way which
is viable for patients,
society, and UCB
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Explosion in Digital Data Sharing
● Social Media: Platforms like X, Facebook,
LinkedIn, YouTube, Instagram, and Reddit
○ Emergence of “influencers” with greater
impact
○ #Hashtags for easier tracking of specific
topics (healthcare hashtag project)
● Blogs, Forums, and Online Communities:
○ Disease-Specific: PatientsLikeMe, patient
advocacy groups, podcasts
○ Healthcare provider review sites:
Healthgrades, ZocDoc, Vitals, RateMDs
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Application of AI in the Life Sciences
Unlocking new,
better, faster
scientific insights
to deliver
differentiated
outcomes
Empowering a
collaborative,
adaptive, and
integrated model
to generate
evidence for a
broader array of
stakeholders
Driving the
evolution from a
stakeholder
centric
healthcare
ecosystem to a
patient-centric
one.
Scientific
Innovation
Evidence
Generation
Patient
Engagement
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GenAI Capabilities
Content Authoring
Generate first drafts of content,
personalize across audiences
Translation,
Transformation, &
Translate languages (e.g
English to French) or into lay
language, transform voice of
content to suit different
audiences or channels, data
format translation from image
to text, text to video, data
redaction and anonymization
Information Retrieval, Q&A
Act as an expert
advisor/coach/assistant,
interface with structured data,
reasoning
Document Review,
Analysis, Summarization
Literature review to generate
summaries, classify sentiment,
generate insights
Search and Traceability
Create easy-to-query
knowledge bases, traceability of
content
Risk Review & Analysis
Document checks against
regulatory and compliance
guidelines
Content Validation
Cross-check content with
validated sources
CREATE
EXTRACT,
SYNTHESIZE,
ANALYZE
COMPLIANCE
REVIEW,
AUDITABILITY
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AI Researchers Using Data from X
● If a pathologist is looking at a path image that's a bit
unusual or ambiguous, they can input it or a text description
into the model to search for similar annotated images
● Viewing similar annotated images may help them make their
differential diagnoses
"The power of such a model is that we don't tell it
specifically what features to look for. It's learning
the relevant features by itself" - Dr. James Zou
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IG Example
“received my first dose of cimzia from
the pharmacy this morning!! my
ophthalmologist is so eager for me to
start this bc my uveitis is not going
away (and im on a weekly visit) so he'll
be happy to know i started my first dose
hihi definitely a lil doubtful of myself bc i
haven't held a syringe in years but i did
well lol *gives self headpats* next dose
will be in 2 weeks!! apparently results
can be seen in abt months, tho some
patients see a decrease in pain and
other symptoms in as little as 2 weeks
so hopefully this works for me i dont
wanna be changing”
NER: Medicine
NER: Condition
Sentiment: Positive
Sentiment: Negative
NER: Specialty
Category: Unit of Measure
Sentiment: Positive
Abbreviation: About
Sentiment: Positive
Sentiment: Negative
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Semantic Shift Bias
● Need to account for semantic shift bias
○ the meaning of words/phrases changes over time
■ Consider modern linguistic norms and cultural context
○ Awful: “inspiring awe” → “terrible” or “bad”
○ Wicked: “evil/morally wrong” →”excellent” or “cool”
○ Bad: “lacking skill or ability” → “unpleasant/undesirable”
○ Egregious: “remarkably good” → “bad” or “shocking”
○ Humble: “low social status” →”modest” or “unpretentious”
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Sentiment Analysis
● Identify common themes (e.g. ease of use, cost, efficacy)
● Identify trends in sentiment over time (e.g. impact of
regulatory changes, product launches, marketing
campaigns)
● Insights provide a deep and holistic understanding of the
disease area ecosystem and stakeholder (incl. patient)
needs as well as the position of our assets
**Insights need to be validated with other data sources (e.g be
reproducible) and actionable
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Pre-Market
● Assess the competitive landscape
● Identify areas of unmet patient need
○ Improve downstream decision making
■ Identify areas for further R&D
■ Additional indications for label expansion
● Better understand patient experiences in trials to ensure
treatments are aligned with patient needs and expectations
● Inform/fine tune product messaging and brand positioning
for launch strategy
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Post-Market
■ Gauge patient satisfaction
■ Identify complaints or concerns about the drug or
its side effects
■ Flag misconceptions or misinformation
● Understand how patients are incentivized to take action
○ Inform patient education and adherence strategies
● Can we decrease latency in time-to-treatment?
● Understand off-label uses of drugs
● Assess patient attitudes towards certain
treatments or medications
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Software as a Medical Device / Smartphone Apps
● Symptom collection and monitoring
● Address delays in diagnosis
○ Decrease time-to diagnosis for undiagnosed patients
● Address disease burden
○ Proactive, action-oriented care management
■ Decrease time to control or prevention of
worsening (e.g. prevention of exacerbations
leading to crises and ER visits)
■ Improve clinical decision support
■ Provide lifestyle suggestions and education
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Example - Ada Health App
● Dynamic conversation in simplified language
● Collects/tracks symptoms and severity in a guided fashion
● Provides intelligent “symptom assessments” which
identify patterns in symptoms
● Offers mail order lab kits for at-home work-ups
● Provides tailored health guidance, treatment option
awareness, and treatment eligibility screening
● Provides at-home drug delivery options, telehealth
● Patient-friendly reports, condition library, in-app articles
● Connects users to a local medical team with detailed clinical
handover
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Example - App for Rare Disease Patients
● AI-driven symptom tracking
○ Objective measures of disease progression and
severity
■ Audio-visual diaries using selfies, video, and audio
recordings → automated measurement and
classification systems
● Measurements were validated with labeling and
annotation protocol
○ Self-reported outcomes using questionnaires
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LLM Driven Interactive Platforms
● Engage patients through interactive and personalized
health management plans
○ Conversational Q&A interface where patients upload
their medical records and “tell their story”
○ Gives patients explanations about their documents
and health data in lay summaries or visualizations
○ Data can be donated for research
■ Recommender engine suggests clinical trials
○ Provides personalized health recommendations
based upon synthesized data inputs
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Digital Biomarkers
● Determine the predictive value of digital
signals to events of interest or clinical
milestones
○ Do measurements look different
immediately preceding or following a
milestone or adverse event?
○ Do digital measurements look different in
people who do not meet a milestone or
have a specific event (e.g. a fall)?
○ Do digital measures correlate with certain
behaviors (e.g. movement or sleep) or
symptoms (e.g. fatigue)?