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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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Becoming more customer and
data-centric in our approach Developing digital platforms for
personalized patient care and
disease identification
Digital mindset Using data and technology
with purpose
Partnering externally for
greater impact
Our digital business transformation journey:
a strategic priority
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Digital
Transformation
Data and Real
World Evidence
Open
Innovation
We continuously innovate
References: 1. UCB 2021 Annual Report.
R&D
28%1
UCB invests about a
quarter of its revenue
in R&D annually
Continual Investment in
Research & Development
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Proprietary and Confidential Property of UCB
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Pivot! Pivot!
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Context Setting
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The Current Paradigm
● Digital exhaust → RWE and
consumer health data are
now outpacing the velocity of
clinical trial data
● Mountains of underutilized
unstructured data
● Advent of a learning
ecosystem, especially with
cloud infrastructure
● Demand for personalization
● Advent of transformer models
and GenAI
RWE/Consumer
Health Data
Evidence
from Clinical
Studies
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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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More Passive Sensing Data To Come!
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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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Framework for Value Back to Patients
Improved
Patient
Insights
Digital Data
Capture &
Processing
Algorithms
autonomously
analyzing data
Actionable Inputs to
Clinical Decision
Making and Care
In near real time To improve patient outcomes
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So what is trending, now?
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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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GenAI Can Be Applied at Each Stage of a Project
Extract data and insights
from text, images, speech
Understand existing work
on the topic, literature
review
Identifying research topics
or gaps in evidence
Communicate the work
(publish, create visuals)
Design project or
experiment
Analyze data, answer
tough questions
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Retrieval Augmented Generation (RAG)
How Do We Ensure Information Veracity?
Prompt or
question
Divide into
smaller
parts
“Chunking”
Prompt
Optimized
From A
Knowledge
Source
Large
Language
Model
Response
Generated
Raw Input
Data
Data in Vector
Form Becomes
an “Embedding”
Retrieving
Context From A
Vector Database
Accurate and
More Relevant
Output in
Desired Format
Synthesis
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Use Case 1:
AI Researchers Using
Medical Twitter
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AI Researchers Using Data from X (Twitter)
● Stanford researchers trained an AI algorithm
on 200,000 high-quality, annotated
pathology images from Medical Twitter
● Images which were paired with 32 hashtags
using language-image contrastive learning
● This collection, named OpenPath, is now one
of the largest public datasets of
human-annotated pathology images
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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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Use Case 2:
Sentiment Analysis
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Primer on Sentiment Analysis
● Uses NLP to go beyond just keyword
identification
● NLP interprets text and infers
emotional states
○ It looks at tone and relationships
between words
○ It classifies the text:
■ positive, negative, neutral
■ Named Entity Recognition (NER)
to identify predefined categories
e.g. persons, organizations,
locations, quantities
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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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Social Listening
● WHAT
○ Patients consider important or
nuanced about their experience as
an unmet need
○ Topics are resonating with
patients and how they are reacting
○ “Language” patients use to
describe their symptoms or patient
journey
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Social Listening
● WHO
○ Patient profiles, personas, segmentation
○ Spheres of influence
■ Who is speaking and who is listening?
■ Who has impact?
● HOW
○ Flow of information across networks
○ How are they generating impact?
● WHY
○ Uncover the why behind beliefs,
behaviors, motivations
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So, what can we DO with this
information?
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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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Use Case 3:
Conversational AI Assistants
and Chatbots
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Conversational AI and Chatbots
Navigating across the patient
journey
● Retrieval and management of health
data and documentation
● Guiding patient through
○ triage, routing, visit intake,
discharge
○ Foster patient empowerment in
making and keeping appointments,
improving patient-physician
communication
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Example - AuxHealth
Conversationally
gathers information
from patients and
summarizes it for
clinicians in a note
sent to EMR or email
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Use Case 4:
Direct-to-Patient
Smartphone Apps (SaMD)
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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
*Class IIa under EU MDR
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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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Ptosis Model
Participants
completed a lid
fatigability
exercise as they
self-recorded a
video on their
smartphone
1. Look directly at
the camera
[3 seconds]
2. Look up while
counting from
1-25
3. Look directly at
the camera
[3 seconds]
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Voice Model
Example Voice Prompts:
○ “papapapa”, “tatatatata”, “kakakaka”, “mamamama” and
“buttercup, buttercup, buttercup” for 4 seconds
○ Hold “aaaahhh” as long as you can
○ “Look at this book with us. It’s a story about a zoo.”
○ “The rainbow is a division of white light into many beautiful colors.”
○ “Mama made some lemon jam.”
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Use Case 5:
LLM Driven Interactive
Platforms
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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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Personal Health Platforms using LLMs
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Aligned Clinical Decision Making
By analyzing diverse patient
experiences which consider a
patient’s lifestyle and social
context, AI can ensure clinical
decision making is aligned with
a specific patient’s
perspectives, preferences,
and expectations
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Treatment Pathway Optimization
AI can suggest optimal treatment
pathways based on the patient’s
characteristics, medical history, and current
clinical guidelines
Knowing treatment history, AI can predict how
individual patients will respond to certain
treatments, minimizing adverse effects and
improving treatment accuracy
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Use Case 6:
Digital Biomarkers
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Digital Biomarkers
Use Case: We can use AI to analyze speech patterns
and perform voice analysis to provide objective and
scalable measures of disease
Collect Voice Sample Utilize Deep Learning in
Semantic & Acoustic
Aspects of Speech
Actionable Results
Delivered to Care Team
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Example - Canary Speech
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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)?
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Digital Measures from Wearables
Endophenotyping, clustering,
classifying population of interest
● Distinguish between different disease
subtypes or phenotypes to better
characterize disease
● Determine if there is something
about a subset of patients that
makes them more or less likely to be
a responder or a fast vs. slow
progressor
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Risk Identification and Stratification
By identifying patterns that may indicate the onset of
conditions, AI can use time-to-event modeling to alert
healthcare professionals and patients early, prevent
complications, and suggest preventive measures
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Outcomes Prediction
Considering likelihood of treatment success, predictive models
can estimate the likelihood of various outcomes
● Complications
● Re-admission
● Infection
● Adverse drug
reactions
● QoL
● Survival
● Disease progression
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Example - Bayesian Health
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Example - Bayesian Health
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How Do We Make Sure AI Does the “Right Thing”?
Good or Bad Reward or Feedback
Reinforcement Learning With Human Feedback (RLHF)
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“Nothing about us, without us”
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UCB
@UCBUSA
facebook.com/UCBbiopharma
www.ucb-usa.com
Thank You!
Proprietary and Confidential Property of UCB
Proprietary and Confidential Property of UCB

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