The Future of the American Healthcare Delivery System in an Era of Change

The Future of the American
Health Care Delivery System in
an Era of Change
Kent Bottles, MD
Chief Medical Officer, PYA Analytics
Thomas Jefferson University School of Population Health
kent@kentbottlesmd.com; 610 639 4956
Health Care Business Intelligence Summit
September 19, 2013

The Presentation
• ACA
• Digital Medicine
• Big Data Analytics
• Social Media

Evidence-Based Medicine &
Patient-Centered Choice
A. Good evidence/
Important to patient
B. Good evidence
C. Potential for
good evidence
D. Important to patient
choice/potential for good
evidence
E. Important to patient
choice/ No potential for
evidence
A B
A. L. Cochrane, from T. Hope Evidence-based patient
choice and the doctor patient relationship in But Will
it Work Doctor? Kings Fund, London 1997, 20 – 24
C
D
E
IMPORTANT
EVIDENCE

CMS The Physician
Feedback/Value-Based Modifier
Program
• The Physician Quality and Research Use
Reports (QRURs)
• The development and implementation of a
Value-based Payment Modifier
• Allows MD to compare quality and cost of
CMS FFS patients’ care with that of other
patients in Iowa, Kansas, Missouri,
Nebraska

CMS The Physician
Program
• Medicare Improvements for Patients and
Providers Act of 2008
• Extended by 2010 Affordable Care Act
• CMS will use the value-based payment
modifier to adjust CMS FFS payments to
physicians based on the quality of care they
furnish compared to the costs of such care

CMS The Physician
Program
• HHS Secretary will phase in program over a
2-year period beginning in 2015
• Beginning in 2017 the value based payment
modifier will apply to all payments made
under Medicare FFS payment schedule

CMS The Physician
Program
• All cost data in your report has been price-
standardized and risk-adjusted to account
for differences in patients’ age, gender,
Medicaid eligibility, and history of medical
conditions, so we make apples to apples
comparisons

CMS The Physician
Program
• COPD
• Bone, joint, muscle
• Cancer
• HIV
• Prevention
• Diabetes
• Gyn
• Heart conditions
• Mental health
• Medication
management

CMS The Physician
Program
• Patients whose care you directed: You
billed 35% or more of all their outpatient
E&M visits.
• Patients whose care you influenced: You
billed less than 35% of outpatient E&M
visits but 20% or more of their costs.
• Patients to whose care you contributed:
You billed less than 35% of visits and less
than 20% of their total costs.

Alternative Methods of Payment
• Fee for service
• FFS and shared savings
• Episode payment
• Partial comprehensive payment and P4P
• Comprehensive (Global payment)
• Capitation

Reducing Costs Without Rationing
Is Also Quality Improvement!
Preventable
Condition
Continued
Health
Healthy
Consumer
No
Hospitalization
Acute Care
Episode
Efficient
Successful
Outcome
Complications,
Infections,
Readmissions
High-Cost
Successful
Outcome

“Episode Payments” to Reward
Value Within Episodes
Preventable
Condition
Continued
Health
Healthy
Consumer
No
Hospitalization
Acute Care
Episode
Efficient
Successful
Outcome
Complications,
Infections,
Readmissions
High-Cost
Successful
OutcomeEpisode
Payment
(“Baskets
of Care”)
$A Single Payment
For All Care Needed
From All Providers in
the Episode,
With a Warranty For
Complications

Yes, a Health Care Provider
Can Offer a Warranty
Geisinger Health System ProvenCareSM
– A single payment for an ENTIRE 90 day period including:
• ALL related pre-admission care
• ALL inpatient physician and hospital services
• ALL related post-acute care
• ALL care for any related complications or readmissions
– Types of conditions/treatments currently offered:
• Cardiac Bypass Surgery
• Cardiac Stents
• Cataract Surgery
• Total Hip Replacement
• Bariatric Surgery
• Perinatal Care
• Low Back Pain

Comprehensive Care Payments
To Avoid Episodes
Preventable
Condition
Continued
Health
Healthy
Consumer
No
Hospitalization
Acute Care
Episode
Efficient
Successful
Outcome
Complications,
Infections,
Readmissions
High-Cost
Successful
Outcome
A Single
Payment
For All Care
Needed For
A Condition
$
Comprehensive
Care
Payment
or
“Global”
Payment

No Additional Revenue
for Taking Sicker
Patients
Payment Levels
Adjusted Based on
Patient Conditions
Providers Lose Money
On Unusually Expensive
Cases
Limits on Total Risk
Providers Accept for
Unpredictable Events
Providers Are Paid
Regardless of the Quality
of Care
Bonuses/Penalties
Based on Quality
Measurement
Provider Makes
More Money If
Patients Stay Well
Provider Makes
More Money If Patients
Stay Well
Flexibility to Deliver
Highest-Value
Services
Flexibility to Deliver
Highest-Value Services
CAPITATION
(WORST VERSIONS)
COMPREHENSIVE
CARE PAYMENT
Isn’t This Capitation?
No – It’s Different

New Roles & Responsibilities
• Hospitals/Specialists
– Reduce volume
– Improve value
• Primary care providers
– Manage costs
– Coordinate patient care
• Consumers
– Manage health, self care
– Choose high-value care

New Roles & Responsibilities
• Health plans
• Change payment systems
• Support providers
• Purchasers
• Change benefit designs
• Pick value-based payers

Kaiser Ids Gaps in MD Readiness
for a Reformed Delivery System
Crosson, Health Affairs, 2011
• Systems thinking
• Leadership and management skills
• Continuity of care
• Care coordination
• Procedural skills
• Office-based practice competencies
– Inter-professional team skills
– Clinical IT meaningful use skills
– Population management skills
– Reflective practice and CQI skills

AHA Physician Leadership Forum:
Competency Development
• Leadership Training
• Systems theory and analysis
• Use of information technology
• Cross-disciplinary training/team building

Competency Development
• Interpersonal and communication skills
– Member of the team
– Empathy/customer service
– Time management
– Conflict management/performance feedback
– Cultural and economic diversity
– Emotional intelligence
• Additional education around
– Population health management
– End of Life/Palliative care
– Resource management
– Health policy and regulation

Competency Development: Gaps
• Systems-based practice: cost-conscious, effective
evidence-based medical care
• Communication skills: effective information
exchange
• Systems based practice: coordinate care with other
providers
• Communication skills: work effectively with
other team members

Competency Development: Missing
• Conflict management/performance feedback
• End of life/palliative care
• Systems theory and analysis
• Customer service/patient experience
• Use of informatics

The ACO Surprise
http://www.oliverwyman.com/the-aco-surprise.htm#.ULTEfqXrWGU
• 25 to 31 million get health care from ACOs
• 2.4 million in CMS ACOs
• 15 million non CMS patients in CMS ACOs
• 8 to 14 million in non CMS ACOs
• More than 40% live in primary care service
areas with at least one ACO

The Digital Revolution in
Medicine

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 web site.”

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.”

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.”

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”

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

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

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

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

Future
• “It is important to approach your health in
general from a lack of understanding.
Honor the body and its relationship to
disease as a complex emergent system that
you may never fully comprehend.”

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

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.”

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

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

Therapies
• Mayo Clinic and Cincinnati Children’s
Hospital studies on cytochrome P450
superfamily of genes and drugs used in treating
mental illness
• We do not understand what causes these
diseases
• GeneSightRx test for 5 genes has allowed
physicians to tailor drug therapy to 12,000
patients’ individual metabolism

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

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

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

Problems with Personalized Medicine
http://www.nejm.org/doi/full/10.1056/NEJMoa1113205?query=featured_home#.T1jNegH6pWY
• 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

The Pros and Cons of Big Data in an
ePatient World

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

Big Data
• Statistics allows richest findings using the
smallest amount of data
• Randomness trumped sample size
• 2007 300 exabytes of stored data
• 2013 1,200 exabytes of stored data
• 2013 only 2% is non-digital

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

Big Data
WSJ March 11, 2013
• 1950s 600 megabytes (John Hancock)
• 1960s 807 megabytes (AA Sabre)
• 1970s 80 gigabytes (Fed Express Cosmos)
• 1980s 450 gigabytes (CitiCorp NAIB)
• 1990s 180 terabytes (WalMart)
• 2000s 25 petabytes (Google)
• 2010s 100 petabytes (Facebook)

Big Data
WSJ March 11, 2013
• 1 Bit = Binary Digit
• 8 Bits = 1 Byte
• 1000 Bytes = 1 Kilobyte
• 1000 Kilobytes = 1 Megabyte
• 1000 Megabytes = 1 Gigabyte
• 1000 Gigabytes = 1 Terabyte
• 1000 Terabytes = 1 Petabyte
• 1000 Petabytes = 1 Exabyte
• 1000 Exabytes = 1 Zettabyte

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 Matt Stairs. Need stat that reflects both runs
produced at bat & runs saved by defense
• WAR (“Wins above replacement”)

Big Data
• To analyze & understand the world we used to test
hypotheses driven by theories
• Big data discards theories & causality for
correlations
• University 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

New Tools to Combat Epidemics
Amy O’Leary, NY Times, June 20, 2013
• Google Flu overestimates spread of flu in 2013
• Goggle Flu does not track new diseases
• BioMosaic
– Combines airline records, disease reports,
demographic data
– Website and iPad app
– Showed 5 counties in Florida, 5 counties in NY
were most at risk from cholera epidemic in Haiti in
2010

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

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

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

Big Data
• GPS allows us to establish location quickly,
cheaply, and without requiring specialized
knowledge
• UPS uses geo-loc data from sensors,
wireless modules, and GPS on vehicles
• 2011 UPS shaved 30 million miles off
routes, saved 3 million gallons of fuel, and
30,000 metric tons of carbon dioxide
emissions

Big Data
• 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

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

Big Data
• 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

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 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

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

Computers Are Just Not That
Smart
• Eric Horvitz, MD of Microsoft
• Medical kiosk avatar interview mother & child
with diarrhea
• Avatar decides child does not need to go to ER
• Avatar makes appointment with clinic
• The moderator of AI panel thought the avatar was
much more compassionate than the human triage
nurses she has encountered in NYC ERs

Vinod Khosla (Sun Microsystems)
http://techcrunch.com/2012/01/10/doctors-or-algorithms/
• Being part of the health care 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.”

Social Media Definition
• The use of web-based and mobile
technology to turn communication into
interactive dialogue

Social Media Definition
• User-generated content
• Web/Health 2.0
• Facebook, Twitter, Linkedin, YouTube
• Blogs
• Forums, chat rooms
• Wikis

Health 2.0
• “Community is the killer app in health care” Steve Case,
Revolution Health
• Web 1.0 users search for and read information
• Web 2.0 regular people create content on line
– Photo-sharing
– Video-uploading
– Music downloading
– Personal blogging
– Podcasts
– Tim O’Reilly 2005

Crowdsourcing
Daren C. Brabham, MIT Press, 2013
• Organization with a defined task or goal
• Community that will perform the task
voluntarily
• Online environment where community and
organization can interact
• Mutual benefit for both organization and
community

Crowdsourcing
• Crowdsourcing is not
– Open source production
– Common-based peer production like Wikipedia
– Market research and brand engagement

Crowdsourcing
• Broadcast search
• Knowledge discovery and management
• Peer-vetted creative production
• Distributed human intelligence tasking

Crowdsourcing
• Distributed human intelligence tasking
• Amazon’s Mechanical Turk
• Farms out tasks like language translation,
survey responses, information gathering, etc.

Crowdsourcing
• Organization asks crowd to create and select
creative ideas
• Sold more than $30 million of silk screened Ts
• Designs created by community
• Community votes on best designs

Crowdsourcing
• Organization tasks crowd to find and collect
information
• Peer-to-Patent
• New York Law School community of 2,600
• Reviewed patent applications for prior art

Crowdsourcing
• Organization tasks crowd with solving
empirical problems
• Innocentive
• Difficult scientific problems broadcast; prize
money to anyone in online community who
comes up with workable solution

Uses of Social Media
• Knowledge Sharing
• Professional Development
• Building Community
• Marketing/Customer Service

• Knowledge Sharing
– Promote awareness about diseases & treatment
– Crowdsource your contacts
– Mentoring and life long learning

• Professional Development
– Dialogue with colleagues
– Learn what patients are saying
– Career development

• Build Community
– Dialogue with patients and answer questions
– Share insights and lessons
– Crowdsource
– Support and advice and mentoring

• Marketing/Customer Service
– Promote services to patients and providers
– Establish online reputation
– Monitor and fix problems before they become
crises

Deloitte Social Networks in Healthcare
http://ow.ly/29QZy
• “Industry stakeholders who do not consider
how to incorporate social networks into
their future strategies risk being run over on
the super-highway of health information
sharing”

Social Media Influence Klout
• Klout score from 1 – 100
• Measures influence by ability to drive
action
• True reach: how many people you influence
• Amplification: how much you influence
• Network impact: influence of your network

Klout Scores of Doctors on
Twitter
• @DrOz: 73
• @sanjaguptacnn: 66
• @NEJM: 57
• @kentbottles: 58

Measuring Social Media
Influence
• Klout
• Peerindex
• Kred
• Radian6

Social Media Best Practices
Mayo Alumni Winter 2012/2013
• “Social media can help us reach people
everywhere without limits of geography or
time zones. Relationships between patients
and providers have changed and are more of
a shared process now. It is incumbent upon
us to put good content in the hands of
people who need it and to engage with
patients beyond the traditional boundaries
of the exam room.” Dr. Farris Timimi

• “We trust physicians with scalpels and
patients’ lives. We can trust you with
Facebook and Twitter.” Dr. Farris Timimi

• Sreenivas Koka, DDS, PhD
• Prosthodontics practice introductory video
• “At the first appointment, the patient now
knows something about me and my
philosophy, and it helps us focus on the
patient’s goals.
• More comfortable, less anxious, more
immediate rapport with patient

• Hartzell Schaff, MD
• YouTube video about hypertrophic
cardiomyopathy surgery
• “I’ve had more inquiries and comments
from patients related to the YouTube video
than any paper I have ever written.”

• Ruben Mesa, MD
• Videos have resulted in more than 50 out-
of-state patients making appointments with
Dr. Mesa at Mayo Clinic Arizona
• 24 videos
– Myelofibrosis 13,000 views
– Polycythemia vera 22,000 views

• Sharonne Hayes, MD
• Women with SCAD met on
www.womenheart.org
• “The group was eager for answers to this
largely unstudied condition and volunteered
itself for research.”
• Registry, DNA Biobank, Study with 400

• Dr. David Farley
• 18 video modules
• Interesting surgery cases
• Surgery pearls of wisdom

http://well.blogs.nytimes.com/2012/10/08/texting-the-
teenage-patient/
• Pediatrician Dr. Natasha Burgert texts pts
• “Better morning with this medication?”
• “Everything is great. Go ahead with plan
we discussed. Please reply so I know you
received”
• “Prepared. Focused. Calm. Your body is
healthy and well. Good luck today.” (SATs)

teenage-patient/
• Pediatrician Wendy Sue Swanson
• Clinic rules forbid texting because it is not
encrypted
• Seattle Mama Doc Blog
• Advises teens to put daily alarm on cell
phone to remind them to take birth control

teenage-patient/
• Dr. Todd Wolynn of Pittsburgh Kids Plus
Pediatrics Practices (19 providers)
• Communications director
– Manages Facebook page
– Website
– Topics (texting in car, invention of chocolate
chip cookie, car seats, sleeping tips)

teenage-patient/
• Mount Sinai Hospital Adolescent Health
Center & SF New Generation Health Center
• STD anonymous email to partners
• Text in the City answers health questions
within 24 hours by a physician

Anna Wilde Mathews, WSJ, February 5, 2013
• Gerry Tolbert, MD, Florence, KY
– Twitter for health messages
– Never tweets about individual patients
• Jen Brull, MD, Kansas rural practice
– Allows patients to be Facebook friends
– “It fits the way I like to practice”
– Will not use social media to send answers to
health questions because of privacy concerns

Anna Wilde Mathews, WSJ, February 5, 2013
• Jake Varghese, GA., family physician
– Uses Kaiser portal
– Will not friend patients on Facebook
• Saroj Misra, Warren, MI, family physician
– Doximity for ortho consult from former
classmate
– Speed via smartphone was “awesome”

Social Media for Small Business
E. Maltby & S Ovide, WSJ, January 31, 2013
• WSJ & Vistage International
• Survey of 835 business owners
• Only 40% have employees dedicated to
social media
• Half spend one to five hours weekly on
social media
• One third spend on time on social media

• Linked In: 30%
• Facebook: 22%
• Twitter: 14%
• YouTube: 13%
• Google+: 7%
• Pinterest: 3%

• Social Media Site with Most Potential
• Linked In: 41%
• YouTube: 16%
• Facebook: 14%
• Google+: 7%
• Twitter: 3%
• Pinterest: 2%

• A2L Consulting
– 800 website visitors a month 2011
– 12,000 website visitors a month 2012
• Small business owners judge social media
– Pageviews
– Click-throughs
– Direct sales

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