Lessons for HealthCare from Consumer   Internet Data        Scott                                     @scootrous          ...
or, Health CareData Science is  Crazy (Fun)
Helping peopleand businesses make better  decisions
Perspective from        consumer internetToday   What is data        science?        Lessons from        LinkedIn for health
Candy!
Data  visualization…what’s                 that? Software from the 80s                           Candy!   Dearth of predic...
Candy?
This is going tobe harder than I  thought…
EHR integration barriers Legal/compliance/privacy  Innovations very hard to   Barriers to quick                    scale  ...
Think like a startup: bias  towards customer feedback, solving for a need, & iteration      Different hats: product       ...
“Data Scientist”means different    things todifferent people
“Data Scientist”                       means different                           things to                       different...
“Data Scientist”means different    things todifferent people
“Data Scientist”                      means different                          things to                      different pe...
“Data Scientist”means different    things todifferent people
My definition of a     data scientist: Someone who uses datato solve problems end-to-end, from asking the right   question...
End-to-end data science: five stages Ask the    Leverage    Extract &                                    Build a  right   ...
One of the hardest Phase 1    things to find in a            data scientist Ask the  right     Health Care: Even for      ...
Phase 2Leverage  othersolutions
Leverage    other disciplinesand intuition
Is model                                  building the                                first thing you                     ...
The g(l)ory of data Phase 3      science: most of the              work is hereExtract andclean your   data
This is what myfriends think I do
This is what I actually do
Health Care  EHR is not designed fordata extraction
LinkedInOn the frontier, but still difficultto do agile data
For most problems,           a wheel has alreadyPhase 4    been invented… Modelbuilding   …just recognize the           wh...
Avoid bogeys by practicingagile analytics
OnlineAdvertising  Uplift Modeling              Credit: Portrait Software
LinkedIn  Skillsuniverse
LinkedIn  Skillsuniverse
Deployment and          execution of          predictive models is          crucialPhase 5Deploy          Central to being...
LinkedInSubscriber   churn reduction
Health Care Population   healthmanagement
Build aviewer app
End-to-end data science: five stages Ask the    Leverage    Extract &                                    Build a  right   ...
Take-aways
Data science is industry-   agnostic
Hugeopportunities, fascinating  problems
Just as physicists moved toWall Street to be quants andthen on to online advertisingand consumer web, there will   be a si...
Thank you!                             Scott (we’re hiring)            Nicholson                                          ...
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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.

Abstract:
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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  • I’m in my 5th month in the health care industry and am really excited about this talk. I’ve given a lot of other talks at data conferences, but this is my first healthcare talk. Before we get started, a little about me: have a PhD in economics from Stanford, studied behavioral economics and did some applied econometric work around understanding biases and fatigue in decision makingWent to online advertising startup, crash course in how to work with dataThen LinkedIn, lead a team of data scientists, worked on user engagement, content & community teams, also worked on economic insights like predicting the unemployment rateNow health care, where I am Chief Data Scientist at Accretive Health. Our main focus is to help hospitals be smarter, from helping them manage their revenue and billing operations on one end to implementing population health management solutions to increase quality and decrease cost on the other.Why do I mention these things? Because it tells you about what is important to me…
  • Might need some clinicians on hand, but that’s one of the lessons…need to work with clinicians, can’t throw tech at the problemMy professional direction is to help….People, cognitive overload, limited information, risk aversion, To show you how legit I am, I’m going to show you the tattoo I got from this brand…j/k, was thinking about bringing it here with a butane torch to heat it up so that we could brand the true data believers, but thought that TSA wouldn’t be so happy with that. So, instead I tried it on steak, didn’t work so well.Data insights are really about overcoming complexity by distilling down information into some simple, actionable form.Two slightly incongruous part of this talk
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • Been in the industry 5 months. First got here and thought:Bad Uis, poor data visuazliation. Not a lotDearth of predictive models to help doctors in real timeSome good graphs could make a huge impact“Oh wow, the EHR looks like THAT?” we can do that, too.Kid in a candy store
  • Lots of good work is being done but not on a scale outside of startups that have a hard time to scale or the elite research institutions. What about critical care hospitals?I’m going to get rich AND help my grandma get better treatment during that next knee replacement…can someone have more than 2 knees?
  • Wait a minute…no easy BI for docs to give them feedback on their decisions? High priority patients?OK I got this, good intuition for data, some hacky engineering skills, can build predictive models…I got this!
  • Good luck starting a company in health care analytics.Plug for accretive here
  • Good luck starting a company in health care analytics.Plug for accretive here
  • Let’s provide a bit more detail about what “end-to-end” means and compare/contrast my experience at LinkedIn to healthcare
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • Parallel for health care: do we need a bunch of fancy machine learning models? Or will a nice data visualization do the job to get people to change their behavior? Context, social relativity,
  • Parallel for health care: do we need a bunch of fancy machine learning models? Or will a nice data visualization do the job to get people to change their behavior? Context, social relativity, ALSO: can you easily deploy some A/B tests to learn some things?
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • When people ask me what I do, they first are fascinated by the title. Then I tell them I’d love to build a robot that helps doctors make better decisions. And they’re thinking, wow, this guy’s a genius!I’m thinking are you kidding me? This is really what I’m doing most of the timePeople don’t want to know about the sausage makingLESSON: need to get other business partners and stakeholders on board. They need to understand that this stuff takes TIME
  • 80% of the workBut you’re having fun doing it…except for this guy. Everyone is sometimes that guy.
  • Parallel for health care: do we need a bunch of fancy machine learning models? Or will a nice data visualization do the job to get people to change their behavior? Context, social relativity,
  • Data in different places, storage types change over time, not communicated, etcWill clean up this imageOpen source…health care has not caught on yetClaudia perlich…doesn’t work with data that she hasn’t pulled herself
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • Online advertising: logistic regression in production at Yahoo for a long timeAgile data. Focus on quick solutions to identify bogeys and get feedbackAndrew Ng: get first model shipped in 24 hours regardless of what it looks like
  • Lesson 1: common sense, subject matter expertise and intuition are crucial for the successful deployment of predictive modelingExample: linkedin scoring models for subscriber churn. Spend a lot of time cleaning, preparing, extracting data. Running models, feature selection, robustness checks, determine best modelProblems: basic insights could have been tested more quickly: credit card expirationProblems: implementation of insights from modelParallel: population health managementCan go out and build a great predictive model for understanding who is most at-risk for ED over-utilization in the next 60 daysWho is going to pay attention to it? Where are the insights going to be surfaced? Will physicians get this infoObservation #1: most of the time the EHR is the devil.Small example how tweaking the decision context can create huge results: petrified forest, social pressure,
  • With the missing charges problem, you need qualified nurse auditors to sign off on suggestions. Kinda like crowdsourcingWhat about in consumer internet? We are crowdsourcing the evaluation of algorithms all the time when people either accept, reject or ignore a recommendation. That exhaust is then used to feed back into the algorithm to improve recommendationsOnly the experts are really anyone and they provide the semi-supervised part of the algorithm
  • With the missing charges problem, you need qualified nurse auditors to sign off on suggestions. Kinda like crowdsourcingWhat about in consumer internet? We are crowdsourcing the evaluation of algorithms all the time when people either accept, reject or ignore a recommendation. That exhaust is then used to feed back into the algorithm to improve recommendationsOnly the experts are really anyone and they provide the semi-supervised part of the algorithm
  • TRANSITIONSummarize, where we areWell that’s great but who is going to do all of that work?
  • One of the fundamental problems of our time18% of GDP! 0.01% is giant revenue potentialData availability and richness only increasingThe right people are realizing data and data science are core to the solution.The best data scientists see the world through the eyes of how data can help solve problems. They are less about a specific algorithm or industry or tool. Thus, their background are all over the placeHIGHLIGHT SOME KEY PROBLEMS IN HEALTH CARECan’t deploy solutions to scale- You have very talented researchers like Pete Szolovits and the CSAIL lab doing great work on decision support. Robots that work with the physician. Even if you can do this pilot in one hospital (MIT), it’s hard to implement this at some meaningful scale. Years vs weeks in consumer internetImmense privacy issuesMost hospitals are non-profit and are focusing on providing care to the community, not on impleeData products are built off of the “data exhaust”, which is easily accessible. In health care, it’s difficult to get the dataData aggregation across hospitals difficult because of competitive concernsIn consumer internet, users get something in return for their data (free product), or they can pay to restrict the usage of their data. What do patients get out of data mining in health care? Not obvious.
  • Plug for accretive
  • Bioinformatics and data science roles. Work on improving the quality of care for patients and making hospitals and physicians smarter. Recently engaged in a really exciting partnership with a nationwide group of private-practice oncologists to help them provide higher quality care while containing cost for patients and payers.
  • 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@ accretivehealth.com lnkd.in/scott
    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 bit.ly/data-science-job snicholson@ accretivehealth.com lnkd.in/scott

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