How Digital & Big Data Revolution Will Transform Primary Care Medicine

November 20, 2014

Incomplete Work Product Prepared for University of North Carolina Physician Network

How Digital & Big Data Revolution Will Transform Primary Care

MedicineKent Bottles, MD & David McMillan

Pershing Yoakley & Associates

University of North Carolina Physicians Network

November 20, 2014

Morrisville, North Carolina

November 20, 2014


Digital Medicine Convergence• Genomics

• Wireless sensors

• Imaging

• Information Systems

• Social networks

• Ubiquity of smartphones

• Unlimited computing power via cloud server farms

November 20, 2014


The Digital Age

• Exponential

• Digital

• Combinatorial

November 20, 2014


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

November 20, 2014



– 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

November 20, 2014



– 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”

November 20, 2014


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.”

November 20, 2014


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.”

November 20, 2014


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

November 20, 2014


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

November 20, 2014


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

November 20, 2014


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

November 20, 2014


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

November 20, 2014


November 20, 2014


Current Rate of Healthcare Spending is Unsustainable

Unsustainable costs drive

not only healthcare reform, but physician payment

discussions

November 20, 2014

Incomplete Work Product Prepared for University of North Carolina Physician Network1616

The Curve

November 20, 2014


Big DataViktor 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

November 20, 2014


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

November 20, 2014


The Amount of Data Availableis 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.

November 20, 2014


Sizing Up Big DataSteve 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

November 20, 2014


Sizing Up Big DataSteve 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

November 20, 2014


Jeffrey Hammerbacherhttp://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

November 20, 2014


Jeffrey Hammerbacherhttp://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

November 20, 2014


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”)

November 20, 2014



• 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

November 20, 2014


Data Treasure Trove in Routine CheckupsWSJ 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

November 20, 2014



• 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

November 20, 2014


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

November 20, 2014


New Tools to Combat EpidemicsAmy 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

November 20, 2014


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

November 20, 2014


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

November 20, 2014


Predictive AnalyticsEric 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

November 20, 2014



• 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

November 20, 2014



• 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

November 20, 2014


When a Health Plan Knows How You ShopNatasha 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

November 20, 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

November 20, 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

November 20, 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”

November 20, 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.

November 20, 2014



• 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

November 20, 2014


Big Data for Cancer CareRon 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

November 20, 2014



• 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

November 20, 2014


Systems Biology Yields New Therapieshttp://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

November 20, 2014


Systems Biology Yields New Therapieshttp://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

November 20, 2014



• 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

November 20, 2014


Spurious Correlations Blogwww.tylervigen.com

November 20, 2014



• 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

November 20, 2014



• 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

November 20, 2014



• 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

November 20, 2014



• 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”

November 20, 2014


What Big Data Can’t DoDavid 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

November 20, 2014


The Hidden Biases of Big Datahttp://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

November 20, 2014


The Hidden Biases of Big Datahttp://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

November 20, 2014


Automate ThisChristopher 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.

November 20, 2014


Automate ThisChristopher 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

November 20, 2014


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 80 th percentile of human physician expertise

November 20, 2014


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”

November 20, 2014


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.”

November 20, 2014


How Robots Will Replace Doctorshttp://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.”

November 20, 2014


How Digital & Big Data Revolution Will Transform Primary Care Medicine

Healthcare

Transcript of How Digital & Big Data Revolution Will Transform Primary Care Medicine