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Data Science Meets Healthcare: The Advent of Personalized Medicine - Jacomo Corbo
1. 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
3. 2
HEALTH EXPENDITURE CONTINUES TO RISE
(SOURCE: National Health Expenditure Database, CIHI)
0
50
100
150
200
250
1975 1980 1985 1990 1995 2000 2005 2010
BillionsofDollars
Actual Spending Inflation-Adjusted Spending ($1997) Forecast
2012f
Average Annual Growth Rates
Actual Spending
Inflation-Adjusted
Spending
1980s 10.8% 4.2%
1990s 4.5% 2.5%
2000ā2010 7.0% 4.2%
4. 3
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%
6%
7%
8%
9%
10%
11%
12%
Actual Forecast
3
5. 4
TOTAL HEALTH EXPENDITURE AS A PROPORTION OF GDP
(SOURCE: National Health Expenditure Database, CIHI; Conference Board of Canada)
4
7. 6
BIG DATA OR THE DIFFERENT FACETS OF MOOREāS LAW
ā¢āÆThe capabilities of many digital electronic devices are strongly linked to
Moore's law: processing speed, memory capacity, sensors
ā¢āÆThe exponential improvement in devices has led to dramatic reductions in
the cost of generating, storing, querying data
ā¢āÆThe development of Big Data āstackā technologies have dramatically
improved our capacity to perform ad hoc queries on very large data sets
12. 11
THE ADVENT OF PERSONALIZED MEDICINE
ā¢āÆNot just about genomic medicine; more so treatments and interventions
tailored to the individual
ā¢āÆEnabled by the advent of āBig Dataā in healthcare: EHR adoption, Big Data
āstackā adoption, rich sensors and APIs in smartphones
ā¢āÆAbove all, it hinges on making effective use of data
15. PREVENTIVE SCREENING IN CANADA AND THE USA
ā¢āÆDemographic-based screening guidelines issued by committees weighing
scientific evidence in both Canada (CTFPHC) and the USA (USPSTF)
ā¢āÆBut demographic markers may be poorly correlated with many conditions
ā¢āÆThere is also increasing awareness of the associated risks of screening
16. NO FREE LUNCH FOR SCREENING:
Ex. 1: Prostate Cancer & PSA: 30,000 deaths/year, treatable
ā¢āÆScreening is not without risk:
āāÆ 70 / 10,000 screenings associated with āminorā complications (infection,
bleeding, urinary difficulties
āāÆ Major complications of Tx: Impotence [40/1,000], MI [2/1,000] DVT [1/1,000]
ā¢āÆHow effective is screening in reducing prostate cancer deaths? To prevent
one death over a 10-year period:
āāÆ Number needed to screen: 1,410
āāÆ Number needed to treat: 48
ā¢āÆUSPSTF: Recommends against screening: "moderate or high certainty that
the service has no net benefit or that the harms outweigh the benefits,ā
ā¢āÆAUA: Favors Screening: āThe American Urological Association (AUA) is
outraged at the USPSTFās 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.ā
17. NO FREE LUNCH FOR SCREENING:
Example 2: Breast Cancer & Mammography
Bleyer & Welsch: āWe 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.ā [NEJM, Nov 2012]
USPSTF: ārecommends 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.ā
ACOG: āDue 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.ā
18. 17
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:
āāÆA machine learning (ML) model with only 6 covariates yields an average
error of 2.7 years for the onset of hypertension
āāÆ Yields a simple screening that ācatchesā hypertension in 98.9% of the
overall population, 100% of most āat riskā patients, and saves ~$275M
USD annually (against the CTFPHC & USPSTFās prescriptions)
Stroke Prediction from the Cardiovascular Health Dataset:
āāÆ ML model with 11 covariates predicts strokes with an average error of
2.3 years
āāÆ Yields a 16% error reduction over best structural models
āāÆML model includes features heretofore unrecognized as risk factors in
literature (e.g. total medications)
21. 20
SURGICAL TEAM PERFORMANCE
w/ S Toms (Geisinger Health System)
ā¢āÆData: 381K surgeries at 16 hospitals over 5 years.
ā¢āÆAnalyze data about surgical team members, how and with whom they
work, to forecast team productivity and patient outcomes, optimize team
assignment.
Highlights:
āāÆDispute conventional wisdom: Inconclusive support for the importance
of individual experience; the only team experience measure that is
significant is tightly-coupled team experience
āāÆDiscover 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
āāÆMake better predictions: We can also predict ~93% of surgeries to
within 15 minutes
25. 24
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
operati
onally
Deploy
early