blue pill or red pill?
Post on 24-Feb-2016
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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..
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