A recent presentation given by PYA Principals Kent Bottles, MD, and David McMillan provides food for thought when it comes to the digital transformation of primary care medicine. The pair spoke at the University of North Carolina Physicians Network on the topic “How Digital & Big Data Revolution Will Transform Primary Care Medicine.”

1. How Digital & Big Data Revolution
Will Transform Primary Care
Medicine
Kent Bottles, MD & David McMillan
Pershing Yoakley & Associates
University of North Carolina Physicians Network
November 20, 2014
Morrisville, North Carolina
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2. Digital Medicine Convergence
• Genomics
• Wireless sensors
• Imaging
• Information Systems
• Social networks
• Ubiquity of smartphones
• Unlimited computing power via cloud server farms
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3. The Digital Age
• Exponential
• Digital
• Combinatorial
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4. The Digital Age
• Exponential
– “The greatest shortcoming of the human race is our inability
to understand the exponential function.” Albert A. Bartlett
– Chess invented in sixth century CE, Gupta Empire
– “Place one single grain of rice on first square of the board,
two on the second, four on the third, and so on.”
– 18 quintillion grains of rice; taller than Mt. Everest
– Numbers so big they are inconceivable
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5. The Digital Age
• Exponential
– ASCI Red fastest computer in world in 1996 ($55 million
and 1600 square feet of floor space)
– 1.8 teraflops of computer speed
– Sony PlayStation 3 in 2005 ($500 and less than a tenth of a
square meter): Sold 64 million units
– 1.8 teraflops of speed
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6. The Digital Age
• Exponential
– Second machine age
– Second half of the chess board
– “into a time when what’s come before is no longer a
particularly reliable guide to what will happen next”
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7. The Digital Age
• Digitalization of everything
– Waze tells you what route is best right now due to network
effort
– Information is non-rival and close to zero marginal cost of
reproduction
– Products are free, perfect, and instant
– “Information is costly to produce but cheap to reproduce.”
Carl Shapiro and Hal Varian
– “I keep saying that the sexy job in the next ten years will be
statisticians. And I’m not kidding.”
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8. The Digital Age
• Digitalization of everything
– Waze tells you what route is best right now due to network
effort
– Information is non-rival and close to zero marginal cost of
reproduction
– Products are free, perfect, and instant
– “Information is costly to produce but cheap to reproduce”
Carl Shapiro and Hal Varian
– “I keep saying that the sexy job in the next ten years will be
statisticians. And I’m not kidding.”
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9. Digital Age
• Combinatorial
– Combining things that already exist
– Kary Mullis 1993 Nobel Prize in Chemistry PCR
– “I thought it had to be an illusion…It was too easy…There
was not a single unknown in the scheme. Every step
involved had been done already.”
– Crowdsourcing with Innocentive or Kaggle
– Waze
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10. The Digital Age
• The emergence of AI and connection of most of the
people on globe via common digital network
• Computers can now demonstrate broad abilities in
pattern recognition and complex communication
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11. The Digital Age
• Watson, Jeopardy, and Medicine
• Human doctor would need to read 160 hours every
week to keep up with relevant new literature
• Freestyle chess tournaments: teams have any
combination of human and digital players
• Weak human + machine + better process superior to
strong computer or strong human + machine +
inferior process
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12. Old New
• Sickness System
• Health: No Disease
• Acute Disease
• Fee for Service
• Hospital Beds Full
• Hospital Centric
• Doctor Centric
• Doctor Decides
• MD defines quality
• Wellness System
• Health: Wellness
• Chronic Disease
• Value Based
• Hospital Beds Empty
• Community Centric
• Patient Centric
• Shared Dec. Making
• Measurable Metrics
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13. Old New
• Cost not considered
• Independent doctors
• Independent
hospital
• Med record secret
• Opaque
• Artificial harmony
• Analogue
• Hypothesis-driven
clinical trials
• Decreased cost
• Employed docs
• Integrated delivery
system
• Open access record
• Transparent
• Cognitive conflict
• Digital
• Predictive analytics
actionable
correlations
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15. Current Rate of Healthcare
Spending is Unsustainable
Unsustainable
costs drive
not only
healthcare
reform, but
physician
payment
discussions
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16
The Curve

17. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Big data refers to things one can do at a large scale
that cannot be done at a smaller one, to extract new
insights or create new forms of value, in ways that
change markets, organizations, the relationship
between citizens and governments
• Causality is replaced by correlation
• Not knowing why but only what
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18. New York City’s Office of Policy
& Strategic Planning
• 1 terabyte of data flows into office every day
• 95% success rate in identifying restaurants dumping
cooking oil into sewers
• Doubled the hit rate of finding stores selling bootleg
cigarettes
• Sped removal of trees toppled by Sandy
• Guided building inspectors to increase citation rate
from 13 to 80% for buildings likely to have
catastrophic house fires
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19. The Amount of Data Available
is Truly Big
• The International Data Corporation reported that the
amount of digital data exceeded 1 zetabyte in 2010.
• In 2011 this number was almost 2 zetabytes.
• Google’s Eric Schmidt claims that every two days
we create as much information as we did from the
dawn of civilization up until the year 2003.
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20. Sizing Up Big Data
Steve Lohr, NY Times, June 20, 2013
• Bundle of technologies
– Web pages, browsing habits, sensor signals, social media,
GPS location data, genomic information, surveillance
videos
– Advances in data storage and processing
– Machine learning/AI software to find actionable correlations
from the big data
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21. Sizing Up Big Data
Steve Lohr, NY Times, June 20, 2013
• Philosophy about how decisions should be made
– Decisions based on data and analysis
– Less based on experience and gut intuition
– Eliminates anchoring bias and confirmation bias
• Revolution in measurement
– Digital equivalent of the telescope
– Digital equivalent of the microscope
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22. Jeffrey Hammerbacher
http://www.youtube.com/watch?v=OVBZTDREg7c
• All industries are being disrupted
– Moneyball, 538, Large Hadron Collider
• McKinsley: Big Data: The Next Frontier for
Competition
– $338 billion potential annual value to US healthcare
o $165 billion in clinical operations
o $105 billion in research and development
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23. Jeffrey Hammerbacher
http://www.youtube.com/watch?v=OVBZTDREg7c
• Oracle: From Overload to Impact
– Healthcare executives say collecting & managing more
business information today than two years ago
– Average increase 85% per year
• Frost & Sullivan: US Hospital Health Data Analytics
Market
– 2011 10% of US hospitals use data analytic tools
– 2016 50% of US hospitals will use data analytic tools
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24. Jeffrey Hammerbacher on Moneyball
www.youtube.com/watch?v=OVBZTDREg7c
• Triple Crown in MLB: Batting average, RBI, HR
• OPS (on base plus slugging)
• GPA (gross production average)
• TOB (times on base)
• The outcome is how many runs we score and allow; A’s
have big, fat, slow Matt Stairs who is terrible outfielder. Need
stat that reflects both runs produced at bat & runs saved by
defense
• WAR (“Wins above replacement”)
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25. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• To analyze & understand the world we used to test
hypotheses driven by theories
• Big data discards theories & causality for
correlations
• Univ. of Ontario premature baby studies
• 1,260 data points per second
• Diagnose infections 24 hours before apparent
• Very constant vital signs indicate impending
infection
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26. Data Treasure Trove in Routine Checkups
WSJ May 13, 2014
• Strep throat score study of 71,776 patients found
230,000 unnecessary doctor visits
• Autoimmune disease patients have fivefold
increased risk of epilepsy
• Association between allergies and flare ups of
uveitis in juvenile idiopathic arthritis
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27. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Google Nature article predicts flu spread in USA
• Compared 50 million search terms with CDC data on
spread of flu from 2003 to 2008
• 450 million different mathematical models
• 45 search terms had strong correlation with spread
of flu
• H1N1 crisis in 2009 Google approach worked
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28. Backlash Against Big Data
• Science article points out Google Flu has over-estimated
number of flu cases for four years
• Inherent biases in how data is collected and
interpreted
• Objection to data mining replacing hypothesis driven
theory by content experts
• Larger the data set the more likely spurious
correlations that are not useful will be identified
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29. New Tools to Combat Epidemics
Amy O’Leary, NY Times, June 20, 2013
• BioMosaic
– Combines airline records, disease reports, demographic
data
– Website and iPad app
– Showed five counties in Florida, five counties in NY were
most at risk from cholera epidemic in Haiti in 2010
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30. Algorithms Mine Public Data
• Atul Butte combined data from 130 studies of gene
activity levels in diabetic & healthy tissue
• Butte identified new gene associate with Type 2 DM
because stood out in 78/130 studies
• Algorithm looking for drugs & diseases that had
opposing effects on gene expression
– Cimetidine for lung adenocarcinomas
– Topiramate for Chrohn’s Disease
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31. Algorithms Mine Public Data
• Russ Altman used algorithms to mine Stanford
Translational Research Integrated Database
Environment & FDA adverse event reports database
• Patients taking SSRI antidepressants and thiazide
are at increased risk for long QT syndrome, a
serious cardiac arrhythmia
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32. Predictive Analytics
Eric Siegel, Wiley, 2013
• Predicting sepsis
– Sisters of Mercy Health System predicts septic shock
based on vital signs observed over time. Detected 71% of
cases with low false-positive rate
• Predicting death
– US health insurance company predicts likelihood person
will die within 18 months to trigger end-of-life counseling on
living wills and palliative care
– Riskprediction.org.uk: predicts your risk of death in surgery
based on your condition
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33. Predictive Analytics
Eric Siegel, Wiley, 2013
• BlueCross BlueShield of Tennessee
– Claims data analysis predicts which health resources
individual member will need in the coming year
• Multicare Health System in Washington State
– $2 million in missed charges a year identified using
algorithm
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34. Predictive Analytics
Eric Siegel, Wiley, 2013
• UPMC
– Predicts patient’s risk of readmission within 30 days of
discharge
• Heritage Provider Network
– $3 million competition to predict number of days patient will
spend in hospital over next year
• BYU & University of Utah
– Correctly predicted 80% of premature births based on
peptide biomarkers found as early as 24 weeks
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35. When a Health Plan Knows How You Shop
Natasha Singer, New York Times, June 28, 2014
• UPMC prediction models using patient claims,
prescriptions, census records, household incomes,
education levels, marital status, race, number of
children, number of cars
• Acxiom, a marketing analytics company
• Mail order shoppers and Internet users are more
likely to use the ER
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36. When a Health Plan Knows How You Shop
Natasha Singer, New York Times, June 28, 2014
• “It brings me another layer of vision, or view, that
helps me figure out better prediction models and
allocate our clinical resources.” Dr. Pamela Peele
• Assigns care coordinators to patients flagged as
high risk
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37. When a Health Plan Knows How You Shop
Natasha Singer, New York Times, June 28, 2014
• Predilytics taps into socioeconomic, demographic,
and consumer purchasing data for health insurers.
• Patients who could not get timely appointments with
PCP and who lacked cars more likely to be
hospitalized.
• “What we are really doing is looking at multifactorial
data and systemic issues that are getting in between
individuals and their ability to maintain the highest
health status.” Chris Coloian
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38. When a Health Plan Knows How You Shop
Natasha Singer, New York Times, June 28, 2014
• MedSeek helps hospitals “virtually influence”
behavior of patients
• Refine marketing pitches based on sex, age, race,
income, risk assessment, culture, religious beliefs,
family status
• Trinity Health System in Michigan used this
company “to scientifically identify well-insured
prospects”
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39. When a Health Plan Knows How You Shop
Natasha Singer, New York Times, June 28, 2014
• Consumer data mining and marketing segmentation
raises ethical issues.
• “Is the larger mission to improve public health, or to
make insurers and hospitals more profitable? I think
we should be careful of running gung-ho into an
area of health care analytics that may disadvantage
deserving patients.” Anita Allen
• Acxiom admits details about consumers can be
wrong.
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40. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Datafication of acts of living
• Zeo large database of sleep patterns
• Asthmapolis sensor to inhaler that tracks location via
GPS identifies environmental triggers
• Fitbit and Jawbone
• iTrem monitors Parkinson’s tremors almost as well
as the tri-axial accelerometer used in specialized
office medical equipment
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41. Big Data for Cancer Care
Ron Winslow, WSJ, March 27, 2013
• ASCO
• Database of hundreds of thousands of patients
• Prototype has collected 100,000 breast cancer
patients from 27 groups who have different EMRs
• “Recognition that big data is imperative for the future
of medicine” Lynn Etheredge
• Less than 5% of adult cancer patients participate in
randomized clinical trials
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42. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Recombinant data
• Danish Cancer Society study on cell phone/cancer
• Cellphone users from 1987 to 1995 (358,403)
• Brain cancer patients (10,729)
• Registry of education and disposable income
• Combining the three databases found no increase in
risk of cancer for those who used cell phones
• Not based on sample size; based on N=all
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43. Systems Biology Yields New Therapies
http://www.nytimes.com/2012/06/03/business/geneticists-research-finds-his-own-diabetes.
html?_r=1&pagewanted=print
• Michael Snyder sequenced his genome that showed
he was at high risk for Type 2 Diabetes
• Blood tests every two months of 40,000 molecules
• After seven months showed he had developed DM
• Early detection, early treatment
• “This study is a landmark for personalized
medicine.” Eric Topol
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44. Systems Biology Yields New Therapies
http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-for-
leukemia-glimpses-of-the-future.html?pagewanted=all
• Dr. Lukas Wartman of Washington University
developed Adult Acute Lymphoblastic Leukemia
• Sequenced cancer cells and healthy cells
• Discovered normal gene in overdrive producing
huge amounts of protein
• Drug for kidney cancer shut down the malfunctioning
gene
• Whole genome sequencing
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45. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Multiple uses of same database
• Data exhaust: digital trail people leave in their wake
• Google spell-checking system uses bad data to
improve search, autocomplete feature in Gmail,
Google Docs, and translation system
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46. Spurious Correlations Blog
www.tylervigen.com
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47. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Paralyzing privacy
– Notice and consent
– Cannot give informed consent for secondary uses
– Anonymization does not work
o AOL 2006 20 million search queries from 657,000 users: NY Times
identified user number 4417749 as Thelma Arnold (“My goodness,
it’s my whole personal life. I had no idea somebody was looking over
my shoulder.”)
o Netflix Prize 100 million rental records from 500,000 users; Mother
and closeted lesbian in Midwest was reidentified
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48. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Probability and punishment
– Minority Report: People are imprisoned not for what they
did, but for what they are foreseen to do, even though they
never actually commit the crime
– Blue CRUSH (Crime Reduction, Utilizing Statistical History
in Memphis, Tennessee
– Homeland Security FAST (Future Attribute Screening
Technology)
– Big data based on correlation unsuitable tool to judge
causality and thus assign individual culpability
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49. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Dictatorship of Data
– Relying on numbers when they are far more fallible than
we think
– Robert McNamara’s body count numbers in Viet Nam
– Michael Eisen tried to buy The Making of a Fly on Amazon
in April 2011; Two established sellers offering the book for
$1,730,045 and $2,198,177; Two-week escalation to a
peak of $23,698,655.93 on April 18
– Unsupervised algorithms priced the books for the two
sellers
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50. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Regulatory shift from “privacy by consent” to
“privacy through accountability”
• “Differential privacy” through deliberately blurring the
data so hard to reidentify people
• Openness, Certification, Disprovability
• Algorithmists to perform “audits”
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51. What Big Data Can’t Do
David Brooks, NY Times, February 26, 2013
• Data struggles with the social
• Data struggles with context
• Data creates bigger haystacks (spurious correlations
that are statistically significant)
• Data has trouble with big problems
• Data favors memes over masterpieces
• Data obscures values
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52. The Hidden Biases of Big Data
http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html
• Big Data vs. Data with Depth
• “With enough data, the numbers speak for themselves.”
Chris Anderson
• Can numbers actually speak for themselves? Sadly,
they can't. Data and data sets are not objective; they
are creations of human design. We give numbers their
voice, draw inferences from them, and define their
meaning through our interpretations.
• Hidden biases in both the collection and analysis stages
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53. The Hidden Biases of Big Data
http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html
• Google Flu Trends vs. CDC
– 11% vs. 6% of US population infected
– Media coverage affected Google Flu Trends
• Boston’s StreetBump smartphone app
– 20,000 potholes a year need to be patched
– Poor areas have less cell phones, less service
• Hurricane Sandy 20 million tweets + 4square
– Grocery shopping day before
– Nightlife peaked day after
– Illusion Manhattan was hub of disaster
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54. Automate This
Christopher Steiner, 2012
• Dr. Bot
– Always be convenient and available.
– Know all your strengths and weaknesses.
– Know every risk factor past conditions might signal.
– Know your complete medical history.
– Know medical history of last three generations of family.
– Never make careless mistake in prescription.
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55. Automate This
Christopher Steiner, 2012
• Dr. Bot
– Always be up to date on treatments and discoveries
– Never fall into bad habits or ruts
– Monitor you at all times
– Always be searching for the hint of a problem by monitoring
pulse, cholesterol, blood pressure, weight, lung capacity,
bone density, changes in the air you expel
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56. Vinod Khosla (Sun Microsystems)
http://techcrunch.com/2012/01/10/doctors-or-algorithms/
• Being part of the healthcare system is a
disadvantage to disrupting the status quo
• Machine learning system will be cheaper, more
accurate, and more objective than physicians
• Machine expertise would need to be in the 80th
percentile of human physician expertise
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57. Vinod Khosla (Sun Microsystems)
http://techcrunch.com/2012/01/10/doctors-or-algorithms/
• Do we need doctors or algorithms
• “Health is like witchcraft and just based on tradition”
• 80% of physicians will be replaced by machines
• 80% of doctors are below the top 20%
• We will not need average doctors
• Still need “doctors like Gregory House who solve
biomedical puzzles beyond our best input ability”
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58. Will Robots Steal Your Job?
http://www.slate.com/articles/technology/robot_invasion/2011/09/will_robots_steal_your_job_3.single.ht
ml
• “At this moment, there's someone training for your
job. He may not be as smart as you are—in fact, he
could be quite stupid—but what he lacks in
intelligence he makes up for in drive, reliability,
consistency, and price. He's willing to work for
longer hours, and he's capable of doing better work,
at a much lower wage. He doesn't ask for health or
retirement benefits, he doesn't take sick days, and
he doesn't goof off when he's on the clock. What's
more, he keeps getting better at his job.”
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59. How Robots Will Replace Doctors
http://www.washingtonpost.com/blogs/ezra-klein/post/how-robots-will-replace-doctors/
2011/08/25/gIQASA17AL_blog.html
• “We’re not sitting in that room wrapped in a garment
made of the finest recycled sandpaper because we
were hoping for a good conversation. We’re there
because we’re sick…, and we’re hoping this
arrogant, hurried, credentialed genius can tell us
what’s wrong. We go to doctors not because they’re
great empaths, but because we’re hoping medical
school has made them into the closest thing the
human race has developed into robots.”
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