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October 2019
A New Era of Personalized Medicine:
The Power of Analytics and AI
Dale Sanders
Asia-Pacific MedTech Forum
© 2019 Health Catalyst
Creative Commons Copyright
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
2
© 2019 Health Catalyst
Creative Commons Copyright
• 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
3
© 2019 Health Catalyst
Creative Commons Copyright
• 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
4
Underground
Command
Center for U.S.
Nuclear
Forces
5
U.S. National
Emergency
Airborne
Command
Post System
“Doomsday
Planes”
© 2019 Health Catalyst
Creative Commons Copyright
Military + Healthcare
• Subjective information from
human sources,
• Objective data from sensors.
• False positives.
• False negatives.
• Time critical.
• Life critical.
Conceptual Decision-Making Similarities
7
© 2019 Health Catalyst
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Digitizing an Industry for
Analytics and AI
Aerospace and Automotive Role Models
8
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?”
9
© 2019 Health Catalyst
Creative Commons Copyright10
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.
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.
11
© 2019 Health Catalyst
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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).
12
© 2019 Health Catalyst
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The machine learning code, in the black box, is a small fraction of
the ML ecosystem.
• 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
© 2019 Health Catalyst
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Spacecraft Health Monitoring – Human Health Monitoring
15
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
© 2019 Health Catalyst
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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/
© 2019 Health Catalyst
Creative Commons Copyright17
“…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
© 2019 Health Catalyst
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Current State
Analytics, AI, and Data in U.S. Healthcare
18
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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
© 2019 Health Catalyst
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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
© 2019 Health Catalyst
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We Are Not “Big Data” in Healthcare, Yet
21
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
© 2019 Health Catalyst
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This is my life.
This is healthcare’s digital
view of my life.
22
Our Digital Understanding of Patients Is Poor
© 2019 Health Catalyst
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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.
23
© 2019 Health Catalyst
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• July 2019
• U of Toronto, Microsoft,
Johns Hopkins, Harvard,
MIT, New York University
24
“…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.”
© 2019 Health Catalyst
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Clinical Text Data: Questionable Quality
25
In a typical note, 18 percent
of the text was manually
entered; 46 percent copied;
and 36 percent imported.
© 2019 Health Catalyst
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EHR Documentation = Observed Physician Behavior
26
• 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
© 2019 Health Catalyst
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The Importance of Outcomes Data
27
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
© 2019 Health Catalyst
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Future State
What does a better state look like?
How do we get there?
28
© 2019 Health Catalyst
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A National Healthcare Goal
29
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.
© 2019 Health Catalyst
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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
© 2019 Health Catalyst
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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.
31
© 2019 Health Catalyst
Creative Commons Copyright32
Biointegrated Electronics–Patient Telemetry
© 2019 Health Catalyst
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Microns-thin, one-inch skin-
pliable sensors with
integrated Bluetooth
antenna, CPU, physiologic
monitors, and wireless
power.
33
© 2019 Health Catalyst
Creative Commons Copyright34
Feb 2019
© 2019 Health Catalyst
Creative Commons Copyright
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
© 2019 Health Catalyst
Creative Commons Copyright
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
36
*—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.
© 2019 Health Catalyst
Creative Commons Copyright
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
37
5. Enabled with direct-to-patient AI and
analytics, the patient and clinician
participate in a joint decision-making
process.
https://clinicaltools.com/
© 2019 Health Catalyst
Creative Commons Copyright
• 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
38
• 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.
© 2019 Health Catalyst
Creative Commons Copyright
• 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.
39
© 2019 Health Catalyst
Creative Commons Copyright
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.
40
© 2019 Health Catalyst
Creative Commons Copyright
• 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…
41

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A New Era of Personalized Medicine: The Power of Analytics and AI

  • 1. October 2019 A New Era of Personalized Medicine: The Power of Analytics and AI Dale Sanders Asia-Pacific MedTech Forum
  • 2. © 2019 Health Catalyst Creative Commons Copyright 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 2
  • 3. © 2019 Health Catalyst Creative Commons Copyright • 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 3
  • 4. © 2019 Health Catalyst Creative Commons Copyright • 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 4
  • 7. © 2019 Health Catalyst Creative Commons Copyright Military + Healthcare • Subjective information from human sources, • Objective data from sensors. • False positives. • False negatives. • Time critical. • Life critical. Conceptual Decision-Making Similarities 7
  • 8. © 2019 Health Catalyst Creative Commons Copyright Digitizing an Industry for Analytics and AI Aerospace and Automotive Role Models 8
  • 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?” 9
  • 10. © 2019 Health Catalyst Creative Commons Copyright10 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. 11
  • 12. © 2019 Health Catalyst Creative Commons Copyright 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). 12
  • 13. © 2019 Health Catalyst Creative Commons Copyright 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
  • 15. © 2019 Health Catalyst Creative Commons Copyright Spacecraft Health Monitoring – Human Health Monitoring 15 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
  • 16. © 2019 Health Catalyst Creative Commons Copyright16 Aircraft Health and Flight Performance Telemetry: It’s All About Creating the Digital Twin https://www.mouser.com/applications/digital-twins-offer-insight/
  • 17. © 2019 Health Catalyst Creative Commons Copyright17 “…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
  • 18. © 2019 Health Catalyst Creative Commons Copyright Current State Analytics, AI, and Data in U.S. Healthcare 18
  • 19. © 2019 Health Catalyst Creative Commons Copyright • 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
  • 20. © 2019 Health Catalyst Creative Commons Copyright20 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
  • 21. © 2019 Health Catalyst Creative Commons Copyright We Are Not “Big Data” in Healthcare, Yet 21 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
  • 22. © 2019 Health Catalyst Creative Commons Copyright This is my life. This is healthcare’s digital view of my life. 22 Our Digital Understanding of Patients Is Poor
  • 23. © 2019 Health Catalyst Creative Commons Copyright 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. 23
  • 24. © 2019 Health Catalyst Creative Commons Copyright • July 2019 • U of Toronto, Microsoft, Johns Hopkins, Harvard, MIT, New York University 24 “…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.”
  • 25. © 2019 Health Catalyst Creative Commons Copyright Clinical Text Data: Questionable Quality 25 In a typical note, 18 percent of the text was manually entered; 46 percent copied; and 36 percent imported.
  • 26. © 2019 Health Catalyst Creative Commons Copyright EHR Documentation = Observed Physician Behavior 26 • 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
  • 27. © 2019 Health Catalyst Creative Commons Copyright The Importance of Outcomes Data 27 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
  • 28. © 2019 Health Catalyst Creative Commons Copyright Future State What does a better state look like? How do we get there? 28
  • 29. © 2019 Health Catalyst Creative Commons Copyright A National Healthcare Goal 29 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.
  • 30. © 2019 Health Catalyst Creative Commons Copyright • 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
  • 31. © 2019 Health Catalyst Creative Commons Copyright This Is the Data We Need… Now Let’s Go After It Standards, sensors, networks, analytics, AI… massive employment and economic opportunity. 31
  • 32. © 2019 Health Catalyst Creative Commons Copyright32 Biointegrated Electronics–Patient Telemetry
  • 33. © 2019 Health Catalyst Creative Commons Copyright Microns-thin, one-inch skin- pliable sensors with integrated Bluetooth antenna, CPU, physiologic monitors, and wireless power. 33
  • 34. © 2019 Health Catalyst Creative Commons Copyright34 Feb 2019
  • 35. © 2019 Health Catalyst Creative Commons Copyright 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
  • 36. © 2019 Health Catalyst Creative Commons Copyright 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 36 *—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.
  • 37. © 2019 Health Catalyst Creative Commons Copyright 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 37 5. Enabled with direct-to-patient AI and analytics, the patient and clinician participate in a joint decision-making process. https://clinicaltools.com/
  • 38. © 2019 Health Catalyst Creative Commons Copyright • 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 38 • 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.
  • 39. © 2019 Health Catalyst Creative Commons Copyright • 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. 39
  • 40. © 2019 Health Catalyst Creative Commons Copyright 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. 40
  • 41. © 2019 Health Catalyst Creative Commons Copyright • 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… 41

Editor's Notes

  1. Healthcare only has a blurred view of the patient based on the data.
  2. Type 2 diabetic patients were clustered around 73 clinical variables. Each node in the graph is a patient and nodes are closer to one another when the two patients (or nodes) exhibit similarities across many clinical variables. The data analysis (similar to clustering) generated 3 distinct subtypes of type 2 diabetes. Subtype 1: Higher concentration of diabetic retinopathy and diabetic nephropathy Subtype 2: Higher concentration of cancer malignancy and cardiovascular disease Subtype 3: Higher concentration of cardiovascular disease, neurological diseases, allergies, and HIV These subgroups were then analyzed with genome data which identified specific genetic variants associated with each different subtype.