Health Care Data Science is Crazy (Fun)! - Scott Nicholson - Strata Rx SF 2012


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These are slides from a talk that I gave at Strata Rx in San Francisco on 10/17/2012.

It is clear that data are core to solving big problems in health care, and data science is the skill set needed to extract insights and make them actionable. Using lessons from experience from consumer internet (LinkedIn & online advertising) and a large dataset of clinical and claims data from across the US, we will discuss results from efforts to increase the quality of care, decreasing cost, and increasing hospital efficiency. Real-world use cases will be presented detailing the use, implementation and impact of deploying predictive analytics.

Examples of use cases to be discussed: - predictive modeling around identifying patients at high risk for overutilization (e.g., many return visits to the ED), allowing for proactive and less expensive care to be provided - using recommendation systems to identify procedures and charges missed during billing, resulting in recovered revenue for the hospital - identifying payer claims likely to be denied and why, to enable more efficient coding of charges - providing rich contextual data for physicians to allow them to maintain or increase the quality of care while decreasing cost

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Health Care Data Science is Crazy (Fun)! - Scott Nicholson - Strata Rx SF 2012

  1. 1. Lessons for HealthCare from Consumer Internet Data Scott @scootrous Nicholson Science snicholson@
  2. 2. or, Health CareData Science is Crazy (Fun)
  3. 3. Helping peopleand businesses make better decisions
  4. 4. Perspective from consumer internetToday What is data science? Lessons from LinkedIn for health
  5. 5. Candy!
  6. 6. Data visualization…what’s that? Software from the 80s Candy! Dearth of predictive modeling EHRs enabling(limited) access to data
  7. 7. Candy?
  8. 8. This is going tobe harder than I thought…
  9. 9. EHR integration barriers Legal/compliance/privacy Innovations very hard to Barriers to quick scale deployment, itera Need technology + onsite tion, ops and impactOpen source does not play well with others
  10. 10. Think like a startup: bias towards customer feedback, solving for a need, & iteration Different hats: product How tomanager, biz dev, sales, data, engg overcome? Think like a data Work closely with customers scientist(docs, patients, hosp. execs...) Leverage expertise to build better models (and be compliant)
  11. 11. “Data Scientist”means different things todifferent people
  12. 12. “Data Scientist” means different things to different peopleCredit: Hilary Mason
  13. 13. “Data Scientist”means different things todifferent people
  14. 14. “Data Scientist” means different things to different peopleCredit: Drew Conway
  15. 15. “Data Scientist”means different things todifferent people
  16. 16. My definition of a data scientist: Someone who uses datato solve problems end-to-end, from asking the right questions to making insights actionable.
  17. 17. End-to-end data science: five stages Ask the Leverage Extract & Build a right other clean Deploy modelquestions solutions your data
  18. 18. One of the hardest Phase 1 things to find in a data scientist Ask the right Health Care: Even for the good ones, havequestions to work closely with clinician partners
  19. 19. Phase 2Leverage othersolutions
  20. 20. Leverage other disciplinesand intuition
  21. 21. Is model building the first thing you should do? Credit: Sam ShahCredit: Sam Shah
  22. 22. The g(l)ory of data Phase 3 science: most of the work is hereExtract andclean your data
  23. 23. This is what myfriends think I do
  24. 24. This is what I actually do
  25. 25. Health Care EHR is not designed fordata extraction
  26. 26. LinkedInOn the frontier, but still difficultto do agile data
  27. 27. For most problems, a wheel has alreadyPhase 4 been invented… Modelbuilding …just recognize the wheel!
  28. 28. Avoid bogeys by practicingagile analytics
  29. 29. OnlineAdvertising Uplift Modeling Credit: Portrait Software
  30. 30. LinkedIn Skillsuniverse
  31. 31. LinkedIn Skillsuniverse
  32. 32. Deployment and execution of predictive models is crucialPhase 5Deploy Central to being able to iterate and have an impact
  33. 33. LinkedInSubscriber churn reduction
  34. 34. Health Care Population healthmanagement
  35. 35. Build aviewer app
  36. 36. End-to-end data science: five stages Ask the Leverage Extract & Build a right other clean Deploy modelquestions solutions your data
  37. 37. Take-aways
  38. 38. Data science is industry- agnostic
  39. 39. Hugeopportunities, fascinating problems
  40. 40. Just as physicists moved toWall Street to be quants andthen on to online advertisingand consumer web, there will be a significant talentmigration into health care in the next few years.
  41. 41. Thank you! Scott (we’re hiring) Nicholson @scootrous snicholson@