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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%
2000–2010 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 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
12. 11
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
15. 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
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 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.”
17. 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.”
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:
– 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)
20. 19
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
21. 20
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
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
