How Lumiata uses AI to manage the complexity of health data, and fast-track healthcare toward value-based care.

1. #healthpredicted
Unpacking AI for Healthcare
@ashdamle
Image from http://bryanchristiedesign.com/

2. We have very little control over
health and care.
From doctors to insurers to patients – we are all
struggling with making sense of health.

3. our health is complex
37+ Trillion Cells

4. Image from http://bryanchristiedesign.com/
We have have no control, and very little visibility
into how health evolves

5. As a result, care management
and coordination is broken &
imprecise, leading to:
higher and higher costs of care
with little improvement in
health outcomes.

6. We have an
opportunity.
High quality data and analytics
can drive precision into
healthcare, reducing costs of
medical care while improving
health outcomes.

7. The challenge:
Healthcare has one of
the most complex
data sets in existence.
High volume. High dimensionality .
Heterogeneous. Varied formats.
Multi-faceted relationships. Noisy.

8. And yet, we are still
using 19th century
solutions for a 21st
century problem!

9. Why not healthcare?
voice recognition, image recognition, natural language processing, deep learning & machine learning
AI has helped many other industries achieve unprecedented levels
of efficiency in overcoming data complexity

10. $6B $2B
The AI market in healthcare will hit
$6 billion by 2020 (Frost and Sullivan)
$2 billion can be saved annually with a
tech-enabled processes (Accenture)
AI is best positioned to solve the health data challenge
AI surfaces the signal from the noise in health data
allowing us to understand what to do, for whom, when, and why
+

11. giving everyone more control and precision over health and care
Automated
information
processing
45% of routine,
manual tasks that
can cost up to
$90 million can
be automated by
adaptingcurrent
AI technologies
(McKinsey).
1
Precise disease
management
Machine learning
could increase
patientoutcomes
at by 50% at
about half the
cost (Indiana
University).
2
Efficient
provider-patient
encounters
Virtual health
appscan save
physicians5 mins
per patient
encounter
(Accenture)
3
Social robots
for patient
engagement
Robots like PARO
have been found
to reduce patient
stress and
interaction with
caregivers
(World Economic
Forum)
4

12. What if we could use AI to predict
future health with precision,
timeliness and speed?
Could we significantly reduce costs of care while creating
more improving outcomes:
less complex, real-time feedback loops, more personalized?

13. How do we get there?
We need real-time machine-based systems that
leverage data to predict health with precision,
timeliness and confidence, so we can deliver
high-value personalized care at scale.

14. It requires…
1.Deep domain expertise in medicine to build robust, clinically-
relevant models
Data science expertise to handle complexity of health data and
apply advanced machine learning techniques
Access to large data sets for supervised and unsupervised
training of models
Infrastructure that can prepare terabytes of data for analysis with
speed
Industry collaboration to build solutions that can be seamlessly
applied into clinical workflows

15. Introducing Lumiata:
an example of Medical AI
that handles the complexity of health data

16. We want to radically transform the
way health data is put to work.
1. Power data-driven precision in predicting health to
reduce costs and improve health outcomes
2. Bring clarity, control and confidence to all health actors

17. Lumiata leverages Medical AI to precisely
predict and manage risk at the individual level.
We drive the personalization and automation
needed to make health predictable.

18. Data Scientists
Utilize the latest in AI & deep
learning to evolve Lumiata’s
MedicalGraph
Design & deploy new models
for targeted use cases
Clinical Scientists
Adjudicate ongoing clinical
inputs into Lumiata’sMedical
Graph
Ensure clinical relevance of
predictive analytics& rationale
DS CS
To build Lumiata, we combine deep domain expertise

19. 330M+ data points describing the
relationships between…
• Hundreds of protocols & guidelines
• 40K+ Symptoms & Signs
• 4K Diagnoses
• 3K Labs, Imaging, Tests
• 3K Therapeutic Procedures
• 7K Medications
across age, gender, durations, lifestyle
Our AI is powered by a learning probabilistic
Medical Graph & Deep Learning
3TB+
unstructured
data
175M+
patient record
years
39K+
physician
curation
hours

20. that predicts individual health risks, and helps
embed personalization and automation in risk
management operations.
Input
(Data)
Analyses
(FHIR+AI)
Output
(Insights)
Delivery
(API)
ImpactAction
Risk Matrix + Clinical RationaleRISK MATRIX
& CLINICAL RATIONALE
MEDICAL GRAPH

21. It augments our ability to identify and capture value in data
by bringing clinical
precision, giving everyone
the confidence to act
with precise health
predictions
by automating labor-
intensive risk
management operations
to reduce costs
(data gathering + data synthesis +
analysis + planning + messaging +
decision + fulfill)
&

22. symptoms diagnoses labs Images
therapy
procedures
meds
environ.
factors,
seasonality
lifestyle +
demo.
profile
geography
past
medical
history
genetics
family
history
vitalscomplaints
∫(age, gender, duration, ethnicity, …)
∫(age, gender, sensitivity, specificity, …)
Generating per patient models of
health, making healthcare delivery
predictable and personalized.
Our Medical Graph maps multi-dimensional relationships to handle
the complexities of health data

23. and by mapping out the relationships of health data, the Medical
Graph address many of the data complexities
in systematic, scalable way
Demographics
Lumiata
Medical
Graph
Procedures
Physical Exam & Tests
Medical & Social Hx
Sensors & Wearables
Genomics
High volume
High dimensionality
Heterogeneous
Varied formats
Multi-faceted relationships
Noisy
Multiple Coding Systems
Graphs not Trees/DAGs

24. PUBMED
References
PUBMED
References
Lumiata
Risk
Matrix
Condition 1 2 3 4 5 6 7 8 …
0-­‐1
Year Y N N Y Y N N N …
1-­‐2
Years Y N N Y Y Y N N …
2+
Years Y N N Y Y Y N Y …
Clinical
Rationale
Clinical
Rationale
Past
Med
History
Diagnoses
Abnormal
Labs
Procedures
Medications
where each prediction is supported with medical evidence,
bringing confidence, control and clarity to health operations

25. 36,000+
Physician
Curation Hours
Clinical Integration Engine Clinical Analytics Engine API & Web Platform
Real-Time Data
Clinical
Financial
Social
Environmental
Descriptive
Introspective
Predictive
Prescriptive
Discovery
Operationalize
Data
Data
Unification
Insight & Action
Generation
Data & Action
Distribution
and transforms data to insight to action

26. Fast-tracking healthcare toward value-based care
Automated risk
stratification to
drive population
health
management
Precise &
personalized
care
management
interventions
Clinical
alignment and
agreement
between payers
and providers
Reduced costs
by removing
labor-intensive,
redundant tasks
+

27. True Clinical State & Risk Evolution
Differential Diagnosis and Triage
Missing Diagnosis
Data Driven Guidelines
Clinically Right Coding (ICD, HCC)
Risk Adjustment
Quality Maximization
Predict High Cost Claimants
Utilization Prediction
Care Coordination
with clear practical use cases available via an API or web app

28. Through AI, we are giving everyone the
confidence to act on data in a way that
improves care, automates processes
and reduces costs.
Health plans become more cost-effective and collaborative.
Caregivers deliver more precise and timely care.
Patients get personalized treatment plans.

29. Image from http://bryanchristiedesign.com/
powering clear, predictable health outcomes

30. #healthpredicted
Unpacking AI for Healthcare
@ashdamle