Data Science Meets Healthcare: The Advent of Personalized Medicine - Jacomo Corbo

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Data Science Meets Healthcare: The Advent of Personalized Medicine - Jacomo Corbo

  1. 1. Data Science Meets Healthcare:The Advent of Personalized MedicineJacomo CorboCanada Research Chair in Information Management, University of OttawaResearch Affiliate, The Wharton School of Business, University of PennsylvaniaChief Scientist, QuantumBlackApril 17, 2013
  2. 2. Healthcare spendinggrowth isunsustainable
  3. 3. 2HEALTH EXPENDITURE CONTINUES TO RISE(SOURCE: National Health Expenditure Database, CIHI)0501001502002501975 1980 1985 1990 1995 2000 2005 2010BillionsofDollarsActual Spending Inflation-Adjusted Spending ($1997) Forecast2012fAverage Annual Growth RatesActual SpendingInflation-AdjustedSpending1980s 10.8% 4.2%1990s 4.5% 2.5%2000–2010 7.0% 4.2%
  4. 4. 3TOTAL HEALTH EXPENDITURE AS A PROPORTION OF GDP(SOURCE: National Health Expenditure Database, CIHI; Conference Board of Canada)8.1%9.7% 9.7%11.9%6%7%8%9%10%11%12%Actual Forecast3
  5. 5. 4TOTAL HEALTH EXPENDITURE AS A PROPORTION OF GDP(SOURCE: National Health Expenditure Database, CIHI; Conference Board of Canada)4
  6. 6. The Big Dataopportunity
  7. 7. 6BIG DATA OR THE DIFFERENT FACETS OF MOORE’S LAW• The capabilities of many digital electronic devices are strongly linked toMoores law: processing speed, memory capacity, sensors• The exponential improvement in devices has led to dramatic reductions inthe cost of generating, storing, querying data• The development of Big Data ‘stack’ technologies have dramaticallyimproved our capacity to perform ad hoc queries on very large data sets
  8. 8. 7THE SEQUENCING EXPLOSION (SOURCE: THE ECONOMIST)
  9. 9. 8
  10. 10. 9MOBILE PHONE AS A SENSOR PLATFORM
  11. 11. 10!"#$%&!"()*+%,#-%,*.*"%#
  12. 12. 11THE ADVENT OF PERSONALIZED MEDICINE• Not just about genomic medicine; more so treatments and interventionstailored to the individual• Enabled by the advent of ‘Big Data’ in healthcare: EHR adoption, Big Data‘stack’ adoption, rich sensors and APIs in smartphones• Above all, it hinges on making effective use of data
  13. 13. Q: So what?A: 2 case studies
  14. 14. Case 1: Targetedpreventive screening
  15. 15. PREVENTIVE SCREENING IN CANADA AND THE USA• Demographic-based screening guidelines issued by committees weighingscientific evidence in both Canada (CTFPHC) and the USA (USPSTF)• But demographic markers may be poorly correlated with many conditions• There is also increasing awareness of the associated risks of screening
  16. 16. NO FREE LUNCH FOR SCREENING:Ex. 1: Prostate Cancer & PSA: 30,000 deaths/year, treatable• Screening is not without risk:–  70 / 10,000 screenings associated with ‘minor” complications (infection,bleeding, urinary difficulties–  Major complications of Tx: Impotence [40/1,000], MI [2/1,000] DVT [1/1,000]• How effective is screening in reducing prostate cancer deaths? To preventone death over a 10-year period:–  Number needed to screen: 1,410–  Number needed to treat: 48• USPSTF: Recommends against screening: "moderate or high certainty thatthe service has no net benefit or that the harms outweigh the benefits,”• AUA: Favors Screening: “The American Urological Association (AUA) isoutraged at the USPSTF’s failure to amend its recommendations on prostatecancer testing to more adequately reflect the benefits of the prostate-specificantigen (PSA) test in the diagnosis of prostate cancer.”
  17. 17. NO FREE LUNCH FOR SCREENING:Example 2: Breast Cancer & MammographyBleyer & Welsch: “We estimated that breast cancer was overdiagnosed(i.e., tumors were detected on screening that would never have led toclinical symptoms) in 1.3 million U.S. women in the past 30 years. Weestimated that in 2008, breast cancer was overdiagnosed in more than70,000 women; this accounted for 31% of all breast cancersdiagnosed.” [NEJM, Nov 2012]USPSTF: “recommends biennial screening mammography for womenaged 50 to 74 years. The decision to start regular, biennial screeningmammography before the age of 50 years should be an individual one andtake patient context into account, including the patients values regardingspecific benefits and harms.”ACOG: “Due to the high incidence of breast cancer in the US and thepotential to reduce deaths from it when caught early, The American Collegeof Obstetricians and Gynecologists (The College) today issued new breastcancer screening guidelines that recommend mammography screening beoffered annually to women beginning at age 40.”
  18. 18. 17DEVELOPING A DATA-DRIVEN SCREENING POLICYw/ N Marko (MD Anderson Clinic), P Ardestani (U of Ottawa), O Koppius(Rotterdam School of Management)Hypertension Onset from the Framingham Heart Study Dataset:– A machine learning (ML) model with only 6 covariates yields an averageerror of 2.7 years for the onset of hypertension–  Yields a simple screening that ‘catches’ hypertension in 98.9% of theoverall population, 100% of most ‘at risk’ patients, and saves ~$275MUSD annually (against the CTFPHC & USPSTF’s prescriptions)Stroke Prediction from the Cardiovascular Health Dataset:–  ML model with 11 covariates predicts strokes with an average error of2.3 years–  Yields a 16% error reduction over best structural models– ML model includes features heretofore unrecognized as risk factors inliterature (e.g. total medications)
  19. 19. Case 2: More e!cienthospitals
  20. 20. 19ORGANIZATIONAL EFFECTS AND LEARNING RATES ONOR UNIT PERFORMANCE• Establish that individual, team,organizational experience matters• Establish evidence for organizationallearning-curve heterogeneity• Moore and Lapré (2012) establishthat 1) individual, team, andorganizational experience, (2)learning-curve heterogeneity (actorslearning at different rates), and (3)workload all simultaneously matter
  21. 21. 20SURGICAL TEAM PERFORMANCEw/ S Toms (Geisinger Health System)• Data: 381K surgeries at 16 hospitals over 5 years.• Analyze data about surgical team members, how and with whom theywork, to forecast team productivity and patient outcomes, optimize teamassignment.Highlights:– Dispute conventional wisdom: Inconclusive support for the importanceof individual experience; the only team experience measure that issignificant is tightly-coupled team experience– Discover what matters: Most significant variable is dyadic teamexperience between chief surgeon and head nurse in knee replacementprocedures; triadic experience between chief surgeon, head nurse, andanesthesiologist for hip replacements– Make better predictions: We can also predict ~93% of surgeries towithin 15 minutes
  22. 22. And Big Scienceapplications
  23. 23. Addressing thegrowing chasmbetween the art of thepossible and reality
  24. 24. 23MAKING EFFECTIVE USE OF DATAAsk theright QTrylotsJoindataThinkofusersMLalgs
  25. 25. 24MAKING EFFECTIVE USE OF DATAAsk theright QTrylotsJoindataThinkofusersMLalgsSourcedataIteratelotsGet therightpeopleThinkoperationallyDeployearly
  26. 26. Q&A

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