Presentation Looks into the Future of Oncology Nursing in a Digital Age

The Future of Oncology in a Digital Age
Kent Bottles, MD
Chief Medical Officer, PYA Analytics
Thomas Jefferson University School of Population Health
April 1, 2014
Scofield Advanced Oncology Nursing Conference
Coralville, Iowa

The End of Illness
David Agus, New York: Free Press, 2011
• “Take a moment to imagine what it would be like
to live robustly to a ripe old age of one hundred or
more. Then, as if your master switch clicked off,
your body just goes kaput. You die peacefully in
your sleep after your last dance that evening. You
don’t die of any particular illness, and you haven’t
gradually been wasting away under the spell of
some awful, enfeebling disease that began years or
decades earlier.”

Jeff Goldsmith on Digital Future
• “David never spent a day in the hospital, and had
one home and two office visits with his physicians
during the course of treatment, which consisted in
its entirety of six weeks’ worth of home infusion
therapy.

Jeff Goldsmith on Digital Future
• The bill for all these services was created,
evaluated, and paid electronically, with David’s
nominal portion of the cost billed to his Visa card,
per agreement with his health plan. He never saw
a paper bill, though he could view the billing
process in real time on his health plan’s website.”

Traditional Medicine
• Diagnose and treat
• Health is defined as absence of disease
• Patient story is subjective and untrustworthy
• Lab results are objective and true
• Pathologists are the most important doctors
• Clinicians are paralyzed until lab provides dx

Traditional Medicine
• Biomedical model reduces every illness to a
biological mechanism of cause and effect
• Attention on acute episodic illness
• Generalists replaced by specialists
• Focus on individuals
• Cure as uncompromised goal
• Focus on disease
• Antibiotics & infectious disease

Digital Medicine of Present & Future
• Predict and Prevent
• Health is a state of complete physical, mental, and
social well-being and not merely absence of
disease
• Patient story is essential for development of
personal metrics which will be unique to each
individual
• Pathologist sadly becomes less important

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 2 months of 40,000 molecules
• After 7 months showed he had developed DM
• Early detection, early treatment
• “This study is a landmark for personalized
medicine.” Eric Topol

Digital Medicine
• Digitizing a human being
– Genome
– Remotely, continuously monitor vital signs, mood,
activity
– Image any part of body, 3D reconstruction, print an
organ
– Readily available on your smartphone, integrated with
traditional medical record, constantly updated

Digital Medicine Convergence
• Genomics
• Wireless sensors
• Imaging
• Information Systems
• Social networks
• Ubiquity of smartphones
• Unlimited computing power via cloud server farms

Rules vs. Complex Tasks
• Tasks that require application of rules by use of
algorithms
• Information processing tasks that cannot be boiled
down to rules
– Pattern recognition
– Complex communication
• The New Division of Labor by Frank Levy and
Richard Murnane, 2004

The Digital Age
• Exponential
• Digital
• Combinatorial

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

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

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”

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.”

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.”

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.”
– Crowdscourcing with Innocentive or Kaggle
– Waze

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

Moravec’s Paradox
• “It is comparatively easy to make computers
exhibit adult-level performance on intelligence
tests or playing checkers, and difficult or
impossible to give them the skills of a one-year
old when it comes to perception and mobility.”
• “Contrary to traditional assumptions, high-level
reasoning requires very little computation, but
low-level sensorimotor skills require enormous
computational resources.”

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

Something Computers Cannot Do
• Ideation
• Coming up with new good ideas or concepts
• Partnership between Dr. Watson and a human
doctor will be far more creative and robust than
either of them working alone
• “You’ll be paid in the future based on how well
you work with robots.” Kevin Kelly

Something Computers Cannot Do
• Jeffrey Dyer and Hal Gregersen interviewed 500
prominent innovators
• Disproportionate number went to Montessori
• Not following rules
• Self motivation
• Questioning
• Doing things a bit differently

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

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.

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

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

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
– $165 billion in clinical operations
– $105 billion in research and development

Jeffrey Hammerbacher
http://www.youtube.com/watch?v=OVBZTDREg7c
• Oracle: From Overload to Impact
– Healthcare executives say collecting & managing more
business information today than 2 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

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”)

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

Big Data
• 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

Big Data
• 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

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

Big Data
• 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

• Agus consulted on treatment of Steve Jobs
• Jobs had both his cancer and normal cells
sequenced for molecular targeted therapy
• Oncologists customized his chemotherapy to
target specific defective molecular pathways in his
tumor
• Treatment changed when tumor mutated during
therapy

• One of Steve Jobs’ doctors said there was hope
that his cancer would soon be considered a
manageable chronic disease, which could be kept
at bay until he died of something else.
• “I’m either going to be one of the first to be able
to outrun a cancer like this, or I’m going to be one
of the last to die from it. Either among the first to
make it to shore, or the last to get dumped.”

Cancer Is Not a Disease of Organs: Turned
on Genes
• Adenocarcinomas with driver mutation for EGFR
gene
• Clinical response with oral med Gefitinib
• Adenocarcinomas with driver mutation Alk+ gene
• Clinical response with Crizotinib
• SCC of lung
• Breast Cancer

Melanoma
• Sixty percent of patients have specific point
mutation (V600E) in the driver mutation BRAF
gene
• 80% response rate when treated with orally active
BRAF mutation directed drug that specifically
binds the mutated protein

Problems with Personalized Medicine
http://www.nejm.org/doi/full/10.1056/NEJMoa1113205?query=featured_home#.T1jNegH6p
WY
• Tumor’s genetic makeup varies significantly
within same tumor sample
• Sampling error may miss genetic mutations that
affect course of disease
• Complicates personalized medicine strategy
• Only 1/3 of 128 mutations were present in all sites
sampled of 4 patients with RCC
• Differences in mutations: primary vs. mets

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 Lymboblastic Leukemia
• Sequenced cancer cells & healthy cells
• Discovered normal gene in overdrive producing
huge amounts of protein
• Drug for kidney cancer shut down the
malfunctioning gene
• Whole genome sequencing

A Catalog of Cancer Genes That’s Done
Carl Zimmer, NY Times, Feb 6, 2014
• Cancer Genome Atlas NIH 2005
• $375 million
• 500 samples from each of 20 cancer types
• Discovered new genes associated with cancer
• Tarceva approved by FDA for lung cancers with
EGFR mutation (10% of nonsmall cell cancers)

• “The Cancer Genome Atlas has been a spectacular
success, there’s no doubt about that.” Bruce
Stillman of Cold Springs Harbor Lab

• Broad Institute of MIT and Harvard propose
completing the atlas
• 100,000 cancer samples need to be analyzed
• “How could we think of beating cancer in the long
term without having the whole catalog?” Eric
Lander of MIT

• Broad Institute of MIT and Harvard propose
completing the atlas
• 4742 samples from 21 cancer types
• Identified 33 new genes identified associated with
cancer that were missed in the original Cancer
Genome Atlas

• “Whether we need to know every cancer gene, I’d
like to see an argument for how that’s going to
help the advancement of new therapy.” Stillman
• “There’s no question that it would be valuable.
The question is whether it’s worth it.” Bert
Vogelstein of Johns Hopkins

Algorithms Mine Public Data
• Atul Butte combined data from 130 studies of
gene activity levels in diabetic & healthy tissue
• Butte identified new gene associated 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

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

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

• 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

• Blue Cross Blue Shield 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

Big Data
• Multiple uses of same database
• Data exhaust: digital trail people leave in their
wake

Big Data
• Paralyzing privacy
– Notice and consent
– Cannot give informed consent for secondary uses
– Anonymization does not work
• 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.”)
• Netflix Prize 100 million rental records from 500,000 users;
Mother and closeted lesbian in Midwest was reidentified

Big Data
• 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

Big Data
• 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.

Big Data
• 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”

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

What Big Data Will Never Explain
http://www.newrepublic.com/article/112734/what-big-data-will-never-explain
• “To datafy a phenomenon,” they explain, “is to
put it in a quantified format so it can be tabulated
and analyzed.”
• Sentiment analysis mathematical model for grief
called Good Grief Algorithm
• “The mathematization of subjectivity will founder
upon the resplendent fact that we are ambiguous
beings. We frequently have mixed feelings, and
are divided against ourselves.”

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

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
– Night life peaked day after
– Illusion Manhattan was hub of disaster

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 3 generations of family
– Never make careless mistake in prescription

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

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

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”

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.”

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.”

References
• The Second Machine Age by Erik Brynjolfsson &
Andrew McAfee, Norton, 2014
• Big Data by Viktor Mayer-Schonberger &
Kenneth Cukier, Houghton Mifflin Harcourt, 2013
• The Creative Disruption of Medicine: How the
Digital Revolution Will Create Better Health Care
by Eric Topol, Basic Books, 2012
• The End of Illness by David Agus, Simon
Schuster, 2013

Presentation Looks into the Future of Oncology Nursing in a Digital Age

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Presentation Looks into the Future of Oncology Nursing in a Digital Age