In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.

1. #healthpredicted
Unpacking AI for Healthcare to Automate Risk and Care Management
@ashdamle | Hive Think Tank
Image from http://bryanchristiedesign.com/

2. our health is complex
37+ Trillion Cells

3. And today, Healthcare feels like a unwinnable game of Tetris
nocontrol&coordination withimprecise outdatedsystems.

4. this is how visible tomorrow’s health is today
Image from
http://bryanchristiedesign.com/

5. Because, healthcare has one of the most complex data sets in existence
High volume . High dimensionality .Heterogeneous
Varied formats . Multi-faceted relationships .Noisy
And why?

6. healthcare lacks visibility, predictability, and precision
which results in a failure of
Timeliness . Alignment . Coordination
And because of this extreme data complexity,

7. Many organizations face challenges in cleaning, standardizing,
normalizing and making sense of longitudinal data.
This leads to an incomplete, outdated view of patients’ health.
Challenge 1
Inability to combine multi-sourced data efficiently and at scale
In 2012, 500 petabyes  by 2020, 25,000+ petabytes.
Effective big data solutions could result in annual industry savings of $300 billion.

8. Healthcare institutions & individuals are taking more financial
risk.
But they fail to minimize underlying health risk because they
cannot
predict what care is needed for whom, when and why.
Challenge 2
An asymmetry between financial and health risk
30% of providers are in risk-sharing agreements, and that figure will double by 2020.

9. Today’s care management processes are costly, labor-intensive, imprecise, and
do not align payers, providers and patients.
And, they have failed to reduce hospitalization for those with chronic illnesses.
Challenge 3
Outdated care management processes
Only 15% of administrative costs on care management, though it impacts 75% of costs
Effective coordination could reduce hospital readmission rates by 10 to 15%.

10. So, why not healthcare?
voice recognition, image recognition, natural language processing, deep learning & machine
learning
Over the last 3 years, AI has helped many other industries achieve
unprecedented levels of efficiency in overcoming data complexity

11. $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)
And healthcare’s problem that AI is best positioned to address
is fixing the precision of risk & care management
AI surfaces the signal from the noise in health data
allowing us to understand what to do, for whom, when, and why
so we can
improve efficiency, reduce costs and deliver precise personalized care.
+

12. And we believe within the next 3 years,
AI will do so same across the healthcare continuum
Automated
information
processing
45% of routine,
manual tasks that
can cost up to $90
million can be
automated by
adapting current AI
technologies
(McKinsey).
1
Precise
disease
management
Machine learning
could increase
patient outcomes at
by 50% at about
half the cost
(Indiana University).
2
Efficient
provider-
patient
encounters
Virtual health apps
can save physicians
5 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

13. 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
However, unpacking AI for risk and care management demands

14. Introducing Lumiata,
an example of unpacking AI for Healthcare
#healthpredicted

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

16. We want to help healthcare institutions
Lumiata is on a mission to power a
virtuous cycle of predictable health through AI
outsmart disease
with
data-driven precision
making health
predictable in real-time
empowering everyone
to act with
control & confidence #healthpredicted

17. And for that
we must build real-time machine-based
systems that enable us to surpass our
limits of precision & timeliness, so we
can
deliver high-value personalized care at
scale
We need to fast-track healthcare
into the ”Fourth Industrial
Revolution”

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

19. to augment our AI’s ability to identify and capture value in data
by automating
risk adjustment, quality metric, & care coordination activities
(currently finding about +$600 on average of additional revenue per patient)
• For those who have bear risk and have data, these activities directly improve both top and bottom
line
• Most risk bearing organizations have care management programs which are ripe for automation
• This gets us the data we need to learn and embedded into workflows for feedback

20. We seek to orchestrate proactive real-time personalized care
by being the interpretive interface
between all actors & data to automate care management activities
data gathering + data synthesis + analysis + planning + messaging + decision + fulfill

21. All towards building a virtuous cycle of AI to create an
end-to-end system that transforms data into insights, and insights into
action.
Data Model Accuracy Communicability Distribution Usage/Feedback
10s of Millions of
Patient Records
Every article in PubMed
38K Physician Hours
Medical Graph
(39M+ Edges)
80%+ PPV across
major conditions
Clear chain of medical
reasoning for each
prediction and suggested
action
Analytic &
Conversational API
to communicate tasks
Active Supervised
Learning
Continuous
improvements
to our models
Data Insight Action

22. 330M+ data points describing the
relationships between…
3TB+ unstructured data || 10s of millions patient records || 36K+ physician
curation hours
• 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

23. symptoms diagnoses labs Images
therapy
procedure
s
meds
environ.
factors,
seasonalit
y
lifestyle +
demo.
profile
geograph
y
past
medical
history
genetics
family
history
vitals
complaint
s
∫(age, gender,
duration,
ethnicity, …)
∫(age, gender, sensitivity,
specificity, …)
This enables us to generate models
on an individual patient level.
which maps multi-dimensional relationships to handle the complexities of health

24. and by mapping out the relationships of health data, the Medical Graph
address many of the data complexities in systematic scalable way
Demographics
Lumiat
a
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

25. Our first step in making health predictable is the Risk Matrix: Time-based, real-
time, personalized predictions on an individual’s risk of chronic disease & events
Lumiata Risk Matrix
Clear clinical rationale provides the
confidence to act
Currently, models are available for:
• Atrial fibrillation
• Bipolar disease
• Chronic kidney disease
• Congestive heart failure
• COPD
• Coronary heart disease
• Dementia
• Depression
• Diabetes Mellitus Type 2
• Obesity
• Primary hypertension
• Rheumatoid Arthritis

26. PUBMED
References
where each prediction is supported with clinical rationale
with highly specific data and links to medical literature
through the Medical Graph with over 39 million edges
Past
Medical Hx
Abnormal
Labs
Procedures
Medications
Clinical
Rationale
Diagnoses
Predicted
Diagnosis #1
PUBMED
References

27. 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
Powering end-to-end, clinically relevant value

28. that addresses tangible challenges across the entire healthcare spectrum
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

29. Identify True Clinical State and 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

30. Population Health
Vendor
> 80% PPV across multiple
conditions over >800K patients
Today,Lumiata’s“L”isembeddedinworkflowsofFortune1000customers
Large Payer
Used in Gaps of Dx, Optimal
Coding, NLP, & Risk Adjustment
200 Health Coaches
>850K members
~$300-$1K on average identified
Large ACO
> 1,100 Users
290K+ Patients
87,231+ Measures closed in 2
months
and proof points on the value of AI powered automation in care
management

31. distributing precise opportunities per patient in real-time
with action taken 60%-70% of the time
because each opportunity is backed by clear medical
rationale

32. 100K feet view
Lumiata
Cloud
Raw Data/Partial
Updates
CSV, JSON, PDF,
CCDA, HL7, API
(Claims, Labs, EHR,
sensors, genetics, …)
Per Patient FHIR Bundle of Input Data
(Data per patient transformed into FHIR,
stnadardized, normalized, and temporally ordered)
… …
Lumiata Risk
Assessment
FHIR Resource
Risk Matrix + Clinical Rationale
developer.lumiata.com

33. unifies knowledge & machine learning
combining 4TB of text, 37K doc hours
& 61M patient records with deep learning
to power hyper-personalized (per patient) models
Differentiated from other approaches through the Medical Graph
Stats:
330M+ data points, 4.2M nodes, 37M edges
100K+ Diagnoses, 70K+ Labs, 10K+ Procedures, 500K+ Meds, 45K+ Symptoms

34. We are humbled to be recognized today as a leader in Medical AI

35. We believe AI’s most transformative impact will be toward a
#healthpredicted world
and Lumiata is building the AI to make health predictable
Image from

36. #healthpredicted
Unpacking AI for Healthcare to Automate Risk and Care
Management
The Hive Think Tank
Ash Damle, Founder & CEO of Lumiata
ash@lumiata.com; @ashdamle