1 / 21

Blue Pill or Red Pill?

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 .”. Blue Pill or Red Pill?.

zeno
Télécharger la présentation

Blue Pill or Red Pill?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Blue Pill or Red Pill?

  2. 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.” Blue Pill or Red Pill? • Morpheus to Neo in the Matrix • Jim Coplien – the Mick Jagger of software engineering who coaches Agile Scrum • Definition of disruption

  3. Do we know what we don’t know?Big Data in Healthcare Dr Chris Tackaberry CEO Clinithink

  4. Healthcare delivery and research challenge 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

  5. Quotable quotes “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” • “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” “I want to re-purpose the EMR data we have collected and make it work for me” • “Physician productivity dropped when we • implemented our EMR, but is OK, the • administrators re-baselined productivity” • “At our facility allergies are recorded in a • Number of different systems as codes or • free text – how do we meaningfully use that?” • “Symptoms, signs and clinical uncertainty is • what 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” • “My patients are sicker than everyone else’s”

  6. Data sources Source: Alexis Borisy

  7. The problem: structure vs. un-structure We are trapped in a transaction processing prison

  8. Some more problems – and solutions

  9. 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]" HaydehPayami, Neurodegenerative Researcher at New York State Dept.ofHealth

  10. 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

  11. 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 not as we know it…

  12. Visualization: one day all data will look like this Boston Transit System

  13. So what?

  14. Ineffective therapies: one size does not fit all Average % of patient population for whom a particular drug in a class is ineffective: Asthma Drugs Arthritis Drugs Cancer Drugs Source: Personalized Medicine Coalition Antidepressants Diabetes Drugs Alzheimer’s Drugs

  15. Genotype - Phenotype convergence: bridging the “omics” Genomics Proteomics “Clinicomics”

  16. Uncertainty Negation Misspelling Family history

  17. Understanding aggregate data NLP creates structured meta-data Analytics dashboards populated

  18. Technology is getting very smart and very plastic (makes the problem worse) These technologies are highly context-aware, powerful, ubiquitous with simple UX All these technologies “guess” through pattern recognition • GOOGLE SEARCHEXPERIENCE/ADWORDS • WOLFRAM ALPHA • IPHONE CONTACTMANAGEMENT SWYPE SHAZAM TRIPIT GPS

  19. Bounding the problem The Airbus A380 has 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)

  20. Natural Language Processing – why now? Linguistics – POS recognition • Horsepower • Reference terminology • Brute force pattern matching • Moons aligning

  21. Summary We think we know what we don’t know Big data have the answers..

More Related