Tim Daines, QuantumBlack

Beyond the hype
Crossing the chasm with AI and
Patient Chronic Disease
Management
@timdaines
@quantumblack
quantumblack
#HXD2019

2All content copyright © 2017 QuantumBlack, a McKinsey company 2All content copyright © 2018 QuantumBlack, a McKinsey company
AI robots working as “co-workers” to
support core tasks of midwives
Human and Machine, not
Human Vs Machine
@timdaines @quantumblack #HXD2019
SOURCE:AZ Damiaan, 2nd March 2019

“Machines Treating Patients? It’s Already
Happening.”
Mabu, an AI robot that talks to heart
failure patients to help them manage
their disease
SOURCE: http://time.com/5556339/artificial-intelligence-
robots-medicine/
March 2019 – Alice Park – Time.com

Medication for large populations
Emergency room and GP own patient data
Doctor knows best
Doctor holds patient-data
Visit a hospital building or room
Big expensive medical equipment
’One Size’ fits all no longer works
SOURCE:www.shutterstock.com, 2nd March 2019

Personal to one or multiple chronic disease(s)
Machine intelligence reduced discovery costs
Patient knows their lifestyle
Patient generates their own data
Virtual visits and online monitoring
Patient owns the data
Personalising health with AI processes

6All content copyright © 2017 QuantumBlack, a McKinsey company
Humans | Machine | Process
AI is very exciting technology and plays a
pivotal role for chronic disease
management ...
… the hard bit is designing for the vision
of how AI will play the role in caring for
the patient’s chronic disease, and the
interplay needed to support the
clinician’s way of working along the
patient pathway

If we want AI in healthcare we need to design for predictive accuracy
Extending experiences beyond websites
and apps where the application of AI
needs to be explained
SOURCE:Garrett, J.J. (2002) Elements of User Experience, 2nd March 2019

Service Plane
Connecting things in healthcare and
broadening to include….
….systems...
….platforms…
….data...
….and product-service ecologies
SOURCE:Cambridge Consultants, 2nd March 2017

Shape plane
Hunting for insights
Or
Needing alerts to interesting
insights
Are chronic disease patients…..

SOURCE: QuantumBlack
Data Science, Engineering and Designer co-collaboration
LEGEND
QuantumBlack Data
Science Process
Business and End-User Input
Process Details
Initial modelling run
‘X’ number of iterations
Knowledge of
the process
Raw
data
Linked
raw data Data
Scientist
creates
variables/features
1,000s of
variables
500
variables
200
variables
ModellingSelect best
variables
from pool
Reduce
variables
20
variables
Business feedback
User Testing feedback
Important
independent
variables
UX Designer and
Data Scientist
Checks impact on
human behaviour,
results and iterates
Random Forest
OLSRegularisation
Forward
stepwise
GLMs
Mutual
Information
Variance
inflation
factors
Top data drivers
& quantification
of impact on
human behaviour
identified
Example Variable: ‘LAST TIME WORKED ON A DRUG DEVELOPMENT’
• User Experience Research
• First, we look at the list of employees who worked on a given batch of a
product
• For each employee, we find the most recent batch of the same product
and calculate the time passed
• The elapsed times are aggregated across all employees on the batch
User testing feedback
+
Business feedback
Panel FTE
Start End Product Batch IDTime Batch Activity
… … … … …… … …
Data
Engineer
finds where
the data lives
and builds
the service
platform
landscape

@timdaines
@quantumblack
quantumblack
Tim’s 101 lessons learnt
for designing AI into patient
pathways
4
#HXD2019

#1 Create a multi-disciplinary team to infuse a culture shift to design thinking
Bringing “old world” stakeholders who are just getting to grips with cloud, with the “new world” stakeholders flexing their AI muscles
Need to fully
understand
how AI works
to trust it
Predictive
performance in
real-life evaluation
trumps
interpretability
Patients
Regulators
Healthcare
professionals
Advocates of interpretability
Tech Engineers
Corporate
decision makers
Analytics
experts
Advocates of performance
C
h
a
s
m

What's the right optimization model for the Human and the Machine?
Human Cognitive
understanding
(Interpretability)
AI and ML Complexity
(Predictive performance)
Neural
network
Support
Vector
Machines
Linear
regression
Decision
tree
Random
forest
HIGH
HIGH
LOW

Designing a future adhering to medication
Interpretability discovery using eco-system mapping
Low
High
1.Chronic Disease Expertise
Medical Experts, Treatment lifestyle habits,
Medical Literature, Technology
2. Cluster the data
Diagnosis, treatment frequency, human
expertise
3. AI and Machine Learning
Learning complex non-linear
relationships between
everything.
SOURCE:Cambridge Consultants, 2nd January 2017

What ‘jobs will be done’ by Human and Machine in this new ecosystem

#2 Map end-to-end experiences where explainable AI (XAI) is needed
Patient downloads
the app, which
guides them to
unbox the Insulin
port
•Patient downloads app
with HCP and leaflet
guidance
•New Smart Port and
CGM device given to
patient
Patient connects app
to Insulin port
seamlessly
•App and leaflet guide the
patient to set-up CGM
and Smart Port durables
(x4)
•Durables flash to
acknowledge connection
Patient uses
introducer needles to
insert soft cannulas
•HCP assists patient in
fitting CGM and Smart
Port consumables to
body, and explains
removal/ replacement
HCP shows patient
how to dose insulin
via port
•HCP explains role of
Smart Port
•HCP illustrates safe
dosing technique and
common pitfalls to avoid
Blood sugar level
appears in app and
web dashboard
•Patient opens app and
views blood sugar level
readings
•Proxied by interstitial
fluid readings
Patient doses
insulin via port
•Patient prepares insulin
and delivery method
(pen, needle, pump)
•Patient doses insulin (or
automated via closed-
loop)
App acknowledges
dose and confirms
accuracy
•Smart Port transmits
delivery volume to phone
in real-time
•Patient sees app
acknowledge dose and
confirm adherence/
Download and
unbox
Set-up and
connect
Fit to body Demo injection Optimizing sugar
intake (XAI)
Dose insulin Re-optimizing sugar
intake (XAI)

Finding the fit end-to-end
1 Generalized linear mode
2 Least Absolute Shrinkage and Selection Operator
Applicability LowMediumHigh
e.g. in this case, a random forest model can achieve high
predictive power, while remaining relatively interpretable
Random Forest
Predictive Power
Scalability
Explainability (XAI)
Bayes Net Neural NetGLM SVM
Type of AI Model
1 2

Tell Stories about what AI could enable…..
….sketch together….
……make prototypes together……
….role-play with a human ‘acting’ as the AI
with a human.
SOURCE:Jamescpai.com, 2nd March 2019
#3 Tell stories to imagine an future state where AI is not in our periphery

Behaviour and data archetypes,
rather than personas
“Asthma is already a burden to
my life every single day. If you
are giving me a piece of
technology that I have to
manually connect to my cell
phone, it’s just going to add
more work.”
Female, 51 – Type II with mild visual impairment

#4 Using the power of LEGO to visualise AI interpretability and performance

Surface discussions around problems in behavioural terms
• What is the behaviour?
• Where does the behaviour
occur?
• Who is involved in
performing the behaviour?
Define metrics for better adherence
outcomes
Trust Transparency Accuracy

Challenges and Opportunities
Performance comes from
human + machine + process
@sg_williams
@quantumblack

Building trust within services powered
by explanatory artificial intelligence
(XAI)
XAI provides a transparent view of
what’s happening, they still need to be
standardized and systematically
assessed
Ethical challenges around the creation
of persuasive systems
Trust with AI

Measuring the effectiveness of AI
interventions
AI doesn’t give you a better truth
But data, advanced analytics models
and human actions give you a feedback
loop to determine behavioural changes

Opportunity to use real-world data for drug development of chronic diseases
a
Target
identification
Target
validation
Preclinical trials
Drug to
the
market
Lead finding
(design/synthesis
of libraries and
screening)
Clinical
trials
Lead optimization
(ok
pharmacokinetic
Profile, toxicity,
mutagenicity)
Drug
Approval
SOURCE:QuantumBlack Data Science, 2nd March 2019

Identifying what drug to make for a rare chronic disease
SOURCE: QuantumBlack

Thank you & We’re Hiring
@timdaines
@quantumblack
quantumblack.com/careers
careers@quantumblack.com
#HXD2019

Tim Daines, QuantumBlack

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