Data Science Meets Healthcare: The Advent of Personalized Medicine Jacomo Corbo Canada Research Chair in Information Management, University of Ottawa Research Affiliate, The Wharton School of Business, University of Pennsylvania Chief Scientist, QuantumBlack

April 17, 2013

Healthcare spending growth is unsustainable

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HEALTH EXPENDITURE CONTINUES TO RISE (SOURCE: National Health Expenditure Database, CIHI)

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1975 1980 1985 1990 1995 2000 2005 2010

Bill

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Actual Spending Inflation-Adjusted Spending ($1997) Forecast

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Average Annual Growth Rates

Actual Spending Inflation-Adjusted

Spending 1980s 10.8% 4.2% 1990s 4.5% 2.5% 2000–2010 7.0% 4.2%

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TOTAL 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%

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Actual Forecast

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TOTAL HEALTH EXPENDITURE AS A PROPORTION OF GDP (SOURCE: National Health Expenditure Database, CIHI; Conference Board of Canada)

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The Big Data opportunity

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BIG DATA OR THE DIFFERENT FACETS OF MOORE’S LAW

• The capabilities of many digital electronic devices are strongly linked to Moore's law: processing speed, memory capacity, sensors

• The exponential improvement in devices has led to dramatic reductions in the cost of generating, storing, querying data

• The development of Big Data ‘stack’ technologies have dramatically improved our capacity to perform ad hoc queries on very large data sets

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THE SEQUENCING EXPLOSION (SOURCE: THE ECONOMIST)

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MOBILE PHONE AS A SENSOR PLATFORM

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THE ADVENT OF PERSONALIZED MEDICINE

• Not just about genomic medicine; more so treatments and interventions tailored 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

Q: So what? A: 2 case studies

Case 1: Targeted preventive screening

PREVENTIVE SCREENING IN CANADA AND THE USA

• Demographic-based screening guidelines issued by committees weighing scientific 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

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 prevent one 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 that the service has no net benefit or that the harms outweigh the benefits,”

• AUA: Favors Screening: “The American Urological Association (AUA) is outraged at the USPSTF’s failure to amend its recommendations on prostate cancer testing to more adequately reflect the benefits of the prostate-specific antigen (PSA) test in the diagnosis of prostate cancer.”

NO FREE LUNCH FOR SCREENING: Example 2: Breast Cancer & Mammography

Bleyer & Welsch: “We estimated that breast cancer was overdiagnosed (i.e., tumors were detected on screening that would never have led to clinical symptoms) in 1.3 million U.S. women in the past 30 years. We estimated that in 2008, breast cancer was overdiagnosed in more than 70,000 women; this accounted for 31% of all breast cancers diagnosed.” [NEJM, Nov 2012]

USPSTF: “recommends biennial screening mammography for women aged 50 to 74 years. The decision to start regular, biennial screening mammography before the age of 50 years should be an individual one and take patient context into account, including the patient's values regarding specific benefits and harms.”

ACOG: “Due to the high incidence of breast cancer in the US and the potential to reduce deaths from it when caught early, The American College of Obstetricians and Gynecologists (The College) today issued new breast cancer screening guidelines that recommend mammography screening be offered annually to women beginning at age 40.”

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DEVELOPING A DATA-DRIVEN SCREENING POLICY w/ 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 average error of 2.7 years for the onset of hypertension

–  Yields a simple screening that ‘catches’ hypertension in 98.9% of the overall population, 100% of most ‘at risk’ patients, and saves ~$275M USD 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 of 2.3 years

–  Yields a 16% error reduction over best structural models

– ML model includes features heretofore unrecognized as risk factors in literature (e.g. total medications)

Case 2: More e!cient hospitals

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ORGANIZATIONAL EFFECTS AND LEARNING RATES ON OR UNIT PERFORMANCE

• Establish that individual, team, organizational experience matters

• Establish evidence for organizational learning-curve heterogeneity

• Moore and Lapré (2012) establish that 1) individual, team, and organizational experience, (2) learning-curve heterogeneity (actors learning at different rates), and (3) workload all simultaneously matter

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SURGICAL TEAM PERFORMANCE w/ S Toms (Geisinger Health System)

• Data: 381K surgeries at 16 hospitals over 5 years.

• Analyze data about surgical team members, how and with whom they work, to forecast team productivity and patient outcomes, optimize team assignment.

Highlights:

–  Dispute conventional wisdom: Inconclusive support for the importance of individual experience; the only team experience measure that is significant is tightly-coupled team experience

–  Discover what matters: Most significant variable is dyadic team experience between chief surgeon and head nurse in knee replacement procedures; triadic experience between chief surgeon, head nurse, and anesthesiologist for hip replacements

–  Make better predictions: We can also predict ~93% of surgeries to within 15 minutes

And Big Science applications

Addressing the growing chasm between the art of the possible and reality

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MAKING EFFECTIVE USE OF DATA

Ask the right Q

Try lots

Join data

Think of users

ML algs

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MAKING EFFECTIVE USE OF DATA

Ask the right Q

Try lots

Join data

Think of users

ML algs

Source data

Iterate lots

Get the right people

Think operationally

Deploy early

Q&A