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 ChangeKent Bottles, MD

Chief Medical Officer, PYA Analytics

Thomas Jefferson University School of Population Health

[email protected]; 610 639 4956

Health Care Business Intelligence Summit

September 19, 2013

mailto:[email protected]

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

CDE

IMPORTANT

EVIDENCE

Affordable Care Act

66

The Curve

6

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


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


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


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


Program• COPD

• Bone, joint, muscle

• Cancer

• HIV

• Prevention

• Diabetes

• Gyn

• Heart conditions

• Mental health

• Medication management


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 RationingIs Also Quality Improvement!

PreventableCondition

ContinuedHealth

HealthyConsumer

NoHospitalization

Acute Care Episode

Efficient Successful Outcome

Complications,Infections,

Readmissions

High-CostSuccessfulOutcome

“Episode Payments” to Reward Value Within Episodes


ContinuedHealth

HealthyConsumer

NoHospitalization

Acute Care Episode



Readmissions

High-CostSuccessfulOutcomeEpisode

Payment(“Basketsof Care”)

$A Single Payment

For All Care Needed From All Providers in

the Episode, With a Warranty For

Complications

Yes, a Health Care ProviderCan 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 PaymentsTo Avoid Episodes


ContinuedHealth

HealthyConsumer

NoHospitalization

Acute Care Episode



Readmissions

High-CostSuccessfulOutcome

A Single Payment

For All CareNeeded ForA Condition

$Comprehensive

CarePayment

or“Global”Payment

No Additional Revenuefor Taking Sicker

Patients

Payment Levels Adjusted Based on Patient Conditions

Providers Lose Money On Unusually Expensive

Cases

Limits on Total RiskProviders Accept forUnpredictable Events

Providers Are Paid Regardless of the Quality

of Care

Bonuses/PenaltiesBased on Quality

Measurement

Provider Makes More Money If

Patients Stay Well

Provider Makes More Money If Patients

Stay Well

Flexibility to DeliverHighest-Value

Services

Flexibility to DeliverHighest-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

AHA Physician Leadership Forum: 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

AHA Physician Leadership Forum: 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

AHA Physician Leadership Forum: 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 Surprisehttp://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 IllnessDavid 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


• “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.”


• 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

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


• 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


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


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


• 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 Medicinehttp://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

http://www.nejm.org/doi/full/10.1056/NEJMoa1113205?query=featured_home#.T1jNegH6pWY



The Pros and Cons of Big Data in an ePatient World

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


• 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 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

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

Big DataWSJ 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 DataWSJ 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 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– $165 billion in clinical operations

– $105 billion in research and development

Jeffrey Hammerbacherhttp://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”)


• 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


• 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 EpidemicsAmy 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


• 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


• 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 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


• 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


• 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


• 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


• 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


• 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


• 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 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

What Big Data Will Never Explainhttp://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 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

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

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 3 generations of

family– Never make careless mistake in prescription

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

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.html

• “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 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.”

Social Media

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

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


• Broadcast search

• Knowledge discovery and management

• Peer-vetted creative production

• Distributed human intelligence tasking




• Peer-vetted creative production

• Distributed human intelligence tasking• Amazon’s Mechanical Turk

• Farms out tasks like language translation, survey responses, information gathering, etc.




• Peer-vetted creative production• 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



• Knowledge discovery and management• 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


• Broadcast search• 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 Healthcarehttp://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 PracticesMayo 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

http://www.womenheart.org/


• Dr. David Farley

• 18 video modules

• Interesting surgery cases

• Surgery pearls of wisdom

Social Media Best Practiceshttp://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

Social Media Best PracticesAnna 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

Social Media Best PracticesAnna 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 BusinessE. 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

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

Health & Medicine

self care

health care provider

highvalue care

patient care consumers

comprehensive care payments

valuebased payment modifier

value based payment

value primary care providers