Healthcare is looking towards an era of personalized medicine in which providers customize treatments for the individual patient. Realizing this tailored level of care s a new level of data volume and analytics and AI capabilities that, while novel to healthcare, other industries are thriving in. Choosing the right role models as healthcare works towards the analytics- and AI-driven territory of personalized medicine will guide informed strategies and establish best practices. With experience and expertise in these key areas, the military, aerospace, and automotive industries can serve as healthcare’s best examples: 1. The human cognitive processes of complex decision making. 2. The digitization of their industries, with the “health” of their assets as key drivers. 3. Operating in a “big data” ecosystem.

1. October 2019
A New Era of Personalized Medicine:
The Power of Analytics and AI
Dale Sanders
Asia-Pacific MedTech Forum

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If, in healthcare, you want a role model for…
• The human cognitive processes of complex
decision making
• Digitization of an industry, motivated by the
“health” of its assets
Look to the military, aerospace, and automotive
industries.
Then hire more electrical engineers. 
Fundamental Message Today
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• My unusual career path to healthcare.
• What does “digitized” look like in the aerospace and
automotive industries?
• The current state of U.S. healthcare AI, analytics, and
data.
• The gap between hype and reality.
• Future state.
• What does it look like and how do we get there?
Today’s Chapters
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• 15 years in space, defense, and national intelligence.
• 22 years in healthcare.
My Background: Data Fusion and Decision Support
B.S. Chemistry,
biology minor
US Air Force Command,
Control,
Communications, &
Intelligence (C3l) Officer
TRW/National Security Agency
• Nuclear weapons policy &
decision making
• START Treaty
• Nuclear non-proliferation
• U.S. nuclear command & control
system threat protection
Director of Medical Informatics,
Intermountain Healthcare
CIO, Cayman Islands
National Health System
CTO, Health
Catalyst
Reagan/Gorbachev
Summits
• Space Operations
• Nuclear Warfare
Planning and
Execution; NEACP &
Looking Glass
CIO, Northwestern
University Medicine
20191983
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5. Underground
Command
Center for U.S.
Nuclear
Forces
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6. U.S. National
Emergency
Airborne
Command
Post System
“Doomsday
Planes”

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Military + Healthcare
• Subjective information from
human sources,
• Objective data from sensors.
• False positives.
• False negatives.
• Time critical.
• Life critical.
Conceptual Decision-Making Similarities
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Digitizing an Industry for
Analytics and AI
Aerospace and Automotive Role Models
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9. 1. Digitize the assets you are
trying to manage and optimize.
Airplanes
Air traffic control,
baggage handling, ticketing,
maintenance,
manufacturing.
Patients
Registration, scheduling,
encounters, diagnosis,
orders, billing, claims.
2. Digitize your operations for
managing the assets you are
trying to understand and
optimize.
What’s Required to Become “Digitized?”
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AI Boils Down to Pattern Recognition
More Cycles = More Data = Better, Faster Pattern Recognition
Patients
Training set: The more times you pass through this loop with
different patient samples, the faster and better you become at
feature extraction, classifying patients, and suggesting
treatments.

11. Data Volume Is Key to AI
“Invariably, simple models and a
lot of data trump more elaborate
models based on less data.”
“The Unreasonable Effectiveness of Data”, March 2009,
IEEE Computer Society; Alon Halevy, Peter Norvig, and
Fernando Pereira, of Google.
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AI Algorithms Are Commodities, Data Platforms Are Not
“…it is dangerous to think of these
quick wins as coming for free. Using
the software engineering framework
of technical debt, we find it is
common to incur massive
ongoing maintenance costs in
real-world ML systems.”
Neural Information Processing Systems (NIPS)
Advances in Neural Information Processing Systems 28 (NIPS 2015).
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The machine learning code, in the black box, is a small fraction of
the ML ecosystem.

14. • Every 10 hours, Tesla collects 1 million miles of
driving data.
• 25Gbytes per car per hour.
• “We can fix problems in your car and make it safer,
long before you know you need it.”
• ”10,000 fatalities and 500,000 injuries per year will
be prevented.”
• Ram Ramachander, Chief Commercial Officer, Social Innovation
Business at Hitachi.
Vehicle Health Monitoring – Human Health Monitoring

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Spacecraft Health Monitoring – Human Health Monitoring
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Properties of Satellite (and Human) Telemetry Data
• High Dimensionality: Hundreds to thousands of data variables.
• Multimodality: Day and night modes; pediatric and adult.
• Heterogeneity: Continuous, real values; discreet, categorical values.
• Temporal Dependence: At what time you collect the data matters; the temporal dimension
between heterogeneous data also matters.
• Missing Data: Is the missing data expected to be missing, or not?
TRW/Northrup Grumman DSP Satellite

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Aircraft Health and Flight Performance Telemetry:
It’s All About Creating the Digital Twin
https://www.mouser.com/applications/digital-twins-offer-insight/

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“…newest generation
aircraft…five-to-eight terabytes
per flight”
“Airplanes like the 787 and A350 collect 10,000 times more data than
1990s or early 2000s-era aircraft. That is because more parameters
are being measured at higher frequencies, using broader transmission
pipelines.”
– Joel Reuter, Vice President of Public Affairs, Rolls-Royce North America

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Current State
Analytics, AI, and Data in U.S. Healthcare
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• Today, the challenges I discuss in
healthcare data do not apply to
imaging or genomics.
• Both are “data dense.”
• Perfect match to leverage the
pattern recognition power of AI.
• We will continue to see significant
value of AI in the imaging and
genomics space.
AI in Imaging and Genomics

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18 Months Versus 10 years
1. Initiated target discovery effort (Wilson’s Disease).
2. Identified the genetic mutation that causes the disease
(Met645Arg).
3. Identified the chemical properties needed in a
molecule to target the mutation.
4. Identified 12 therapeutic agents.
26 Sep 2019
• Brenden Frey, CEO, PhD
• Computer Engineering
• Neural Nets

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We Are Not “Big Data” in Healthcare, Yet
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Citation: Dale Sanders, CIO, Northwestern
Medicine. Calculating annual storage
requirements for the Northwestern electronic
health record, 2011.
5-8TB per 4 hrs
30TB in 8 hrs

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This is my life.
This is healthcare’s digital
view of my life.
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Our Digital Understanding of Patients Is Poor

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The Human Health Data Ecosystem
• In the U.S., our digital view of
the patient is stuck in the
lower left quadrant.
• On average, we collect data
on patients about 3 times per
year in the U.S., during visits
to the clinic or hospital.
• We collect almost no data on
healthy patients, who rarely
visit the healthcare system.
Turn this into your strategic data acquisition roadmap.
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• July 2019
• U of Toronto, Microsoft,
Johns Hopkins, Harvard,
MIT, New York University
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“…diseases in EHRs are poorly labeled,
conditions can encompass multiple
underlying endotypes, and healthy
individuals are underrepresented. This article
serves as a primer to illuminate these
challenges and highlights opportunities for
members of the machine learning community to
contribute to healthcare.”

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Clinical Text Data: Questionable Quality
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In a typical note, 18 percent
of the text was manually
entered; 46 percent copied;
and 36 percent imported.

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EHR Documentation = Observed Physician Behavior
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• 38.5 percent of review of systems (ROS) were
confirmed (61.5 percent of the time, the EHR data did
not reflect reality).
• 53 percent of physical exams (PE) were confirmed (47
percent of the time, the EHR data did not reflect
reality).
• Sept 2019
• UCLA, Stanford, UC Santa Cruz
Perception
Reality

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The Importance of Outcomes Data
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49 percent of randomized clinical
trails were deemed high risk for
wrong conclusions because of
missing or poor measurement of
outcomes data.
18 Sep 2019

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Future State
What does a better state look like?
How do we get there?
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A National Healthcare Goal
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By 2030, every citizen will possess
at least 10,000 times more data, coupled
with analytics and AI, to support their
health optimization, than exists in 2020.
In the U.S., that means going from 100MB to 1TB per year.

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• That end state is a productive,
creative workforce.
• Shift the national healthcare debate:
• From the soft assertion that healthcare
is a human right.
• To the hard assertion that healthcare
is a fundamental requirement of a
thriving workforce.
• The economy is the dominant factor
in U.S. political elections.
Economically, Healthcare Is a Means to a National Ends

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This Is the Data We Need… Now Let’s Go After It
Standards, sensors, networks, analytics, AI… massive employment and
economic opportunity.
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Biointegrated Electronics–Patient Telemetry

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Microns-thin, one-inch skin-
pliable sensors with
integrated Bluetooth
antenna, CPU, physiologic
monitors, and wireless
power.
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Feb 2019

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Creating the Patient’s Digital Twin
Developing three fundamental AI pattern
recognitions in healthcare
Patients like
this
[pattern].
Who were
treated like
this
[pattern].
Had these
outcomes
and costs
[pattern].
hpcwire.com

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Enabling the Digital Healthcare Conversation
"I can make a health optimization recommendation for you, informed
not only by the latest clinical trials, but also by local and regional data
about patients like you; the real-world health outcomes over time of
every patient like you; and the level of your interest and ability to
engage in your own care. In turn, I can tell you within a specified range
of confidence, which treatment or health management plan is best
suited for a patient specifically like you and how much that will cost.”*
Between a physician and their patient… or patient and their avatar
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*—Inspired by the Learning Health Community
We are parsing this statement for outcomes and cost data, predictive analytics,
machine learning, social determinants of health data,
recommendation engines.

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1. Enabled by bio-integrated sensors,
patients hold more data about themselves
than the healthcare system holds.
2. Their data is constantly being updated and
uploaded to cloud-based AI algorithms.
3. The algorithms diagnose the patient’s
condition, calculate composite and specific
health risk scores, prognosis, and
recommend treatment options.
4. The algorithm recommends options for a
“best fit” care provider, location,
community support partnerships.
5. Recommends social support connections
with “patients like you.”
Future of Diagnosis and Treatment
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5. Enabled with direct-to-patient AI and
analytics, the patient and clinician
participate in a joint decision-making
process.
https://clinicaltools.com/

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• Different patient types have different
data profiles required for the active
management of their outcomes and
health.
• I’m not talking about quality
measures.
• I’m talking about telemetry,
diagnostics, and functional status
about the state of the patient, not the
state of healthcare processes.
Rise of the Digitician and Patient Data Profiles
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• It’s the digitician’s job to prescribe the right
sensors and proactively collect this data for
patients in their panel and feed the analytics
of that to the care team and patient.

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• Mt. Sinai Hospital, 2015.
• Visualizes and explores clusters of
patients grouped together by
algorithms.
• 2,551 Type 2 diabetic patients
clustered on 73 clinical variables.
• A rules-based approach would not
find these subgroups.
Three New Diabetic Subtypes from Pattern Recognition
Li L, Cheng W-Y, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis
of patient similarity. Science translational medicine. 2015;7(311):311ra174.
doi:10.1126/scitranslmed.aaa9364.
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Automatic Diagnosis: Neural
Networks (CNN) Applied to EHR
• Tsinghua University, Beijing, 2017.
• Automatic diagnosis without artificial
construction of rules or knowledge
bases.
• Automatically extract semantic
information.
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• Be humble healthcare… look for
role models, borrow concepts
and hire engineers from military,
aerospace, and automotive.
• The volume and quality of
healthcare data is lower than the
hype would lead you to believe.
• The good news: much is left to
achieve, and transformation is
truly ahead in front of us.
In Closing…
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