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Blue Pill or Red Pill?

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Blue Pill or Red Pill?Blue Pill or Red Pill?“You take the blue pill, the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill, you stay in Wonder-land, and I show you how deep the rabbit hole goes.”

· Morpheus to Neo in the Matrix· Jim Coplien – the Mick Jagger of software engineering

who coaches Agile Scrum · Definition of disruption

powered by:powered by:

Dr Chris Tackaberry

CEO Clinithink

Do we know what we don’t know?Big Data in Healthcare

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Healthcaredelivery and

researchchallenge

In pursuit of improved outcomes and research, HIT systems and data help us....• Discover things and gain

genuine insight• Reduce risk, manage

uncertainty• Reduce uncertainty,

manage risk• Live with risk, cope with

uncertainty• None of the above

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Quotable quotes

“My patients are sicker than everyone else’s”

“We don’t really know what the risk factors

are, the answer is in the data but we have

no way of pulling it out or even trending it”“Symptoms, signs and clinical uncertainty iswhat I deal with everyday, not ICD codes”“If you want me to see the same number of

patients [or more] don’t give me click-itis” “Physician productivity dropped when we

implemented our EMR, but is OK, the

administrators re-baselined productivity”

“At our facility allergies are recorded in aNumber of different systems as codes orfree text – how do we meaningfully use that?”

“There is an excess mortality and morbidity

from cervical cancer in the local population

which we can’t explain. I wish we could mash

up all the data from all the sources and really

see what’s going on”

“I want to re-purpose the EMR data wehave collected and make it work for me”

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Data sources

Public Genomic Datasets

Private Genomic Datasets

Phenomic Datasets

Registries

Healthcare DatasetsSource: Alexis Borisy

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The problem: structure vs. un-structure

We are trappedin a transactionprocessing prison

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Some more problems – and solutions

• BYOD (Bring Your Own Data)We need to get the data and lots of it

• Data scientists, and their toolsWe need to curate it

• Ontologies, horsepower, clever stuffWe need to enrich it

• Heatmaps, all manner of widgets We need to visualize them

• Hmmm…We need to know what questions we want to ask

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BYOD – crowd-driven research(Bring Your Own Data)

"What 23andMe did in a matter of years would have taken several decades and tens of millions of dollars [if done conventionally]"

Haydeh Payami, Neurodegenerative Researcher at New York State Dept.of Health

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When I grow up I want to be a data scientist

“Just as physicists moved to Wall Street to be quants and then on to online advertising and consumer web, there will be a significant talent migration into health care in the next few years”

Scott Nicholson, Data Scientist, Accretive Health

Source:Drew Conway

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Clever stuff: pulling structure out of narrative

“slight cough. violent sneezing. very sore eyes both sides. Eczema in childhood. Mother has asthma, no FH atopy otherwise. OE: Moderate conjunctival injection on left. Tympanic membranes pink both sides. Impression: probably hay fever. Possibly has trivial left AOM too.”

Its data Jim, but notas we know it…

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Visualization: one day all data will look like this

BostonTransitSystem

powered by:powered by:

So what?

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Ineffective therapies: one size does not fit all

Source:PersonalizedMedicineCoalition

Average % of patient population for whom a particular drug in a class is ineffective:

Asthma Drugs Arthritis Drugs Cancer Drugs

Antidepressants Diabetes Drugs Alzheimer’s Drugs

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Genotype - Phenotype convergence: bridging the “omics”

Genomics Proteomics “Clinicomics”

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UncertaintyNegationMisspellingFamily history

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Understanding aggregate data

NLP creates structured meta-data

Analyticsdashboardspopulated

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Technology is getting very smart and very plastic (makes the problem worse)All these technologies “guess” through pattern recognition

· GOOGLE SEARCHEXPERIENCE/ADWORDS

· WOLFRAM ALPHA

· IPHONE CONTACTMANAGEMENT

These technologies are highly context-aware, powerful, ubiquitous with simple UX

SHAZAM SWYPE

TRIPIT GPS

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Bounding the problem

The Airbus A380has about 4 million parts, with 2.5 million part numbers produced by 1,500 companies from 30 countries

(Source: Airbus)

“Left fractured neck of femur”Snomed CT: 1.4 million terms;compositional grammar; modifiers, qualifiers and primary concepts

(Rector AL, Nowlan WA, Kay S. Foundations for an electronic medical record. Methods Inf Med. 1991 Aug;30(3):179–186)

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Natural Language

Processing – why now?

Linguistics – POS recognition• Horsepower• Reference terminology• Brute force pattern

matching• Moons aligning

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Summary

There is a ton of data out there and its growing

exponentially

Much of it is narrative: Patients and their physicians

tell and record stories

Much of it will come from the consumer(with permission)

Innovative technologies and skills are emerging to

provide actionable insights

Benefits in healthcare performance, research and

surveillance

We think we know what we don’t knowBig data have the answers..