With reform in healthcare and advancements in technology, the future of medicine is in a state of flux. What it all means can be heard in discussions from coast-to-coast, in the halls of hospitals, at conferences, and in board rooms. Among the thought leaders who have broached this timely subject is PYA Principal Kent Bottles, MD, who is also PYA Analytics’ Chief Medical Officer. He recently spoke at The North American Menopause Society Annual Meeting on the topic: “The Perils and Prospects of Practicing Medicine in a Digital Era.”

1. Perils & Prospects of Practicing
Medicine in a Digital Era
Kent Bottles, MD
Chief Medical Officer, PYA Analytics
Thomas Jefferson University School of Population Health
kent@kentbottlesmd.com; 610 639 4956
NAMS 2013 Annual Meeting
Plenary Symposium 1

2. The Digital Revolution in
Medicine

4. 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

5. 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

6. 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.

7. 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 web site.”

8. 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.”

9. Eric Topol on MI prevention
• “Monitoring would ideally use an implanted
nanosensor, smaller than a grain of sand
and capable of finding its targets in even
one-millionth of a liter of blood,
communicating with a patient’s
smartphone. Individuals who would get the
nanosensors would be those whose genome
sequence or other biomarkers had already
put them at risk for a heart attack.

10. Eric Topol on MI prevention
• Well before the horse was out of the barn,
the nanosensor could alert the individual to
seek attention; therapy then would consist
of both anti-clotting and anti-inflammatory
medications. At some point in the future,
nanosensors will likely have the capacity to
release medications on their own in
response to high levels of circulating cells
or nucleic acids”

11. Digital Medicine Convergence
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Genomics
Wireless sensors
Imaging
Information Systems
Social networks
Ubiquity of smartphones
Unlimited computing power via cloud server
farms makes Big Data Analytics possible

12. Data Breaches
http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/
• Texas Health Harris Methodist Hospital
– Microfiche not destroyed by paper shredder vendor
winds up in public park
• SC Department of Health (230,000)
– 17 Excel spreadsheets illegally copied
• Emory HealthCare (315,000)
– Unlocked storage room 10 backup disks lost
• Utah Department of Health (780,000)
– Weak passwords

13. Data Breaches
http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/
• 495 breaches
• Average: 42,659
• Average time to id:
84.78 days
• $4.1 billion cost
• Average cost: $8 mill
• Average time to
notify: 68.31 days

14. Data Breaches
http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/
• If more than 500 pts affected must report
• Devastating Public Relations impact
• One class action lawsuit is seeking $4.9
billion in damages
• $1000 per patient affected by breach

15. EHR Associated Medical Errors
• National Ambulatory Medical Care Survey
found EHRs were not associated with better
quality ambulatory care
• PA Pt Safety Authority found 324 medical
errors due to HER
– Default settings
– Wrong time errors
– Automated stop medications errors

16. EHR Associated Medical Errors
• Vendor contract barriers don’t allow
discussion of errors between providers
• Usability problem of too many clicks
• Coding software problems
• System glitches
• Keys/bars too close together
• Backfiring features

17. EMR Facilitate Data Mining
• OIG interest in cardiac procedures
• Heart stents, implantable cardioverterdefibrillators, pacemakers
• St. Joseph Medical Center in Towson, MD
• Stent rates well above state average

18. EMR Facilitates Data Mining
http://articles.baltimoresun.com/2013-10-04/health/bs-md-co-stent-trial-openings-20131004_1_mark-midei-midei-and-st-catholichealth-initiatives
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Mark Midel, MD has settled 250 cases
45 malpractice cases pending
Hospital sold due to financial difficulties
Hospital paid $22 million in fines to CMS
Hospital revenues dropped 25%
Dr. Midel state medical license revoked

19. 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

20. Digital Medicine of Present & Future
• Human body and disease is complex
emergent system that may never be fully
understood
• Attention on chronic diseases
• Managing chronic diseases rather than cure
• Focus on person and the disease

21. Digital Medicine of Present & Future
• 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

22. Digital Medicine of Present & Future
• 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.”

23. Systems Biology Yields New Therapies
• Zoledronic acid affects bone metabolism
and is used to reduce fractures but does
nothing to cancer cells.
• Zoledronic acid decreases breast cancer
recurrence by 36% presumably because it
changes the environment of bones so cancer
cells cannot spread.

24. Systems Biology Yields New Therapies
http://www.nytimes.com/2012/06/03/business/geneticists-research-finds-his-owndiabetes.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

25. Systems Biology Yields New Therapies
http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-forleukemia-glimpses-of-the-future.html?pagewanted=all
• Dr. Lukas Wartman of Washington
University developed Adult Acute
Lymphoblastic 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. Big Data
Viktor Mayer-Schonberger & Kenneth Cukier, 2013
• Paralyzing privacy
– Notice and consent
– Consent for secondary uses impossible
– Anonymization does not work
• AOL 2006 20 million search from 657,000 users:
NY Times 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.

36. 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 Vietnam
– 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.

37. 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

38. The Hidden Biases of Big Data
http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html
• 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

39. 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