Germination Project Data Science at Penn Medicine

Transcript

1. Data Science and Predictive Healthcare
   Corey Chivers, PhD
   Senior Data Scientist
   @CjBayesian
   

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2. 2
   Outline
   wWhat is Data Science?
   wBecoming a data scientist
   wTools of a data scientist
   wPredictive Healthcare at Penn Medicine
   

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3. 3
   wWhat is Data Science?
   

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4. 4
   wWhat is Data Science?
   wWhat do you need to study to
   become a Data Scientist?
   

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5. 5
   Data Science Venn Diagram
   http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
   

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6. 6
   Popular Expectations
   

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8. 8
   Using data and computation
   to give people superpowers
   

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9. 9
   One Data Scientist’s Story
   w Undergraduate Chemistry McGill
   University
   

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10. 10
    One Data Scientist’s Story
    w Undergraduate Chemistry Atmospheric Physics McGill
    University
    

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11. 11
    One Data Scientist’s Story
    w Undergraduate Chemistry Atmospheric Physics
    Environmental Science
    McGill
    University
    

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12. 12
    One Data Scientist’s Story
    w Undergraduate Chemistry Atmospheric Physics
    Environmental Science
    w Non-Profit research & activism around public space use in
    Toronto
    McGill
    University
    

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13. 13
    One Data Scientist’s Story
    w Undergraduate Chemistry Atmospheric Physics
    Environmental Science
    w Non-Profit research & activism around public space use in
    Toronto
    w PhD Computational Biology
    McGill
    University
    

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14. 14
    One Data Scientist’s Story
    w Undergraduate Chemistry Atmospheric Physics
    Environmental Science
    w Non-Profit research & activism around public space use in
    Toronto
    w PhD Computational Biology
    Modeling
    population
    dynamics for
    conservation
    McGill
    University
    

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    The medical record is full of data*
    *There’s a whole bunch that is not in the EHR, too!
    There are all kinds of
    patterns in there!
    Provides predictions to aid in clinical decision making
    

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20. 20
    “Predictive Health Care” is the
    development and integration of Data
    Science* into clinical and operational
    processes and workflows to deliver
    better outcomes at lower cost.
    * Machine Learning, data visualization, computational simulation, AI, etc.
    

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21. 21
    Cat
    Cat
    Cat
    Dog
    Dog
    Doge
    Machine Learning
    Generalizing from many examples
    

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22. 22
    0.7 Dog
    0.25 Cat
    0.05 …
    Cat
    Dog
    Machine Learning
    Generalizing from many examples
    

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    Time
    Encounter with First
    Diagnosis of Valve
    Disease
    (‘ground truth’ ICD code)
    Patient Encounters
    Diagnoses (non-valve disease)
    Labs
    Etc.
    Input data
    Time
    Input data
    Positive
    Case
    Negative
    Case
    No encounters with
    diagnosis of Valve
    Disease
    (‘ground truth’ ICD code)
    Clinical Example
    

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24. 24
    Penn Medicine Data
    Data Type 2011-Present
    estimated Total
    Vital (Manual) 20GB
    Vital (Telemetry) 1.5TB/year
    Labs 36 GB
    Meds and Orders 42 GB
    Radiology Raw 250TB
    Radiology Meta 200 GB
    Clinical Notes 250 GB/year
    Twitter 100TB
    Other social media (est.) 1TB
    Wearables (est.) 5GB/year
    Genomics 800TB
    Lots of examples to generalize!!
    

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    Data Visualization
    

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26. 26
    Data Visualization
    

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27. 27
    Data Visualization
    

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    Computational Simulation
    Using randomization to predict
    trajectories of complex dynamic
    systems
    Allows you to experiment with ‘what
    if’ scenarios and to select from
    alternative actions.
    ‘Writing down’ the hypothesized
    system dynamics also sharpens
    understanding and assumptions.
    (Chivers, 2014)
    

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    w Creating ‘Digital Twins’ that allow a team to explore efficiency gains
    • Build optimized scheduling templates
    • Test process changes to drive clinical workflow redesign
    • Identify bottlenecks
    w We’ve created digital twins to support Emergency Department and
    OB/GYN clinic operations
    Patient
    Volume
    Minute
    Census
    Wait
    Times
    OB/GYN Simulated Output
    Computational Simulation
    

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    Tools
    

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    Skills
    Successful Data Science is highly collaborative
    

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    As a ____________
    ROLE
    when I’m ____________
    CONTEXT
    if I knew ____________
    INFORMATION
    I would do _____________
    INTERVENTION
    to improve __________
    OUTCOME
    Data Science
    “MadLibs”
    Solving the right problem
    

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    Mechanical Ventilation
    

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    If I knew when mechanical
    ventilator patients were ready
    for a Spontaneous Breathing
    Trial (SBT)
    Mechanical Ventilation
    

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    I would reduce sedation and
    initiate an SBT trial earlier to
    decrease how much time
    patients spend in the ICU
    Mechanical Ventilation
    

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    Time on Ventilation
    Mechanical Ventilation
    Time in
    ICU
    

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    Lung Connect
    

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    If I knew which lung cancer
    patients will go to the ED
    Lung Connect
    

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    I would address their
    symptoms in the outpatient
    setting to lower ED usage
    Lung Connect
    

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    flic.kr/p/aDbss
    E
    Lower ED Usage
    Lung Connect
    

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    https://www.pennmedicine.org/news/news-blog/2018/june/palliative-connect-digitizing-the-physicians-intuition-to-prompt-critical-conversations
    Palliative Connect
    

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    https://www.pennmedicine.org/news/news-blog/2018/june/palliative-connect-digitizing-the-physicians-intuition-to-prompt-critical-conversations
    If I knew which patients
    have serious, life-limiting
    illnesses
    Palliative Connect
    

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    I would make sure they receive
    a palliative care consultation
    to ensure the care team
    understands their goals and
    desires
    https://www.pennmedicine.org/news/news-blog/2018/june/palliative-connect-digitizing-the-physicians-intuition-to-prompt-critical-conversations
    Palliative Connect
    

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    Earlier access to
    palliative care
    More documented
    advanced care plans
    Palliative Connect
    

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    Some Challenges
    w Data != Reality
    • Healthcare data is particularly challenging
    – Non-random missingness
    – Largely unstructured
    – Clinical concepts and their representations change over time
    w How good does the model need to be?
    • We use meta decision theory to decide whether the model makes
    better decisions than some alternative
    w How do we know it’s working?
    • When you’re trying to prevent the thing you’re predicting, is your
    prediction bad or your intervention good?
    

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    Changing Clinical Concepts
    Weekly Count
    

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    How good does the model need to be?
    Data Scientist:
    Finally, I built a model from the data!
    Doc:
    Awesome, is it good?
    Data Scientist:
    I used tensorflow, so, ya.
    Doc:
    Lets deploy this thing!*
    *Any resemblance between these fictional characters and any persons, living or dead, is a purely coincidental
    

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    How good does the model need to be?
    Data Scientist:
    Finally, I built a model from the data!
    Doc:
    Awesome, is it good?
    Data Scientist:
    I used tensorflow, so, ya.
    Doc:
    Lets deploy this thing!*
    *Any resemblance between these fictional characters and any persons, living or dead, is a purely coincidental
    

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    All models are
    wrong, but some
    are useful.
    - George Box
    

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50. 50
    FP
    FN TP
    TN
    

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    How good tho?
    How good tho?
    How bad tho?
    How bad tho?
    FP
    FN TP
    TN
    

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    Goodness can be measured in any units
    More adorbs Less adorbs
    

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    When outcomes are uncertain, the best decision
    is the one that has the highest expected
    goodness.
    

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    When outcomes are uncertain, the best decision
    is the one that has the highest expected
    goodness.
    Machine Learning
    can only help you
    with this part!
    

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    When outcomes are uncertain, the best decision
    is the one that has the highest expected
    goodness.
    Machine Learning
    can only help you
    with this part!
    

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    Healthcare Example
    Predicted Sepsis
    Treated a true
    case
    (Potential to avoid
    bad outcome)
    Predicted Sepsis
    Treated a false
    case
    (unnecessary)
    Predicted No
    Sepsis
    Didn’t treat
    (all good)
    Predicted No
    Sepsis
    Failed to treat
    (Bad outcome)
    

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    Healthcare Example
    

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    Treat none
    Cost of Intervention ($)
    Cost of event ($)
    Treat all
    Pregnancy Related Hypertension (PRH) is the leading
    cause of maternal morbidity and mortality in the U.S.
    High-risk patients à remote blood pressure monitoring
    https://healthcareinnovation.upenn.edu/projects/heart-safe-motherhood
    

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    Takeaways
    w Predictive Healthcare is the integration of Data
    Science into clinical and operational workflows to
    improve healthcare quality
    w Data Science is a collaborative, multidisciplinary
    endeavor
    • Using data to make better decisions
    • There are many paths to becoming a data scientist!
    w Taking time to ensure you’re solving the right
    problem is essential
    

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60. 60
    Thanks!!
    

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