Germination Project Data Science at Penn Medicine

Germination Project Data Science at Penn Medicine

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Corey Chivers

August 05, 2019
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Transcript

  1. 2.

    2 Outline wWhat is Data Science? wBecoming a data scientist

    wTools of a data scientist wPredictive Healthcare at Penn Medicine
  2. 4.

    4 wWhat is Data Science? wWhat do you need to

    study to become a Data Scientist?
  3. 7.

    7

  4. 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
  5. 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
  6. 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
  7. 15.

    15

  8. 16.

    16

  9. 17.

    17

  10. 18.

    18

  11. 19.

    19 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
  12. 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.
  13. 22.

    22 0.7 Dog 0.25 Cat 0.05 … Cat Dog Machine

    Learning Generalizing from many examples
  14. 23.

    23 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
  15. 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!!
  16. 28.

    28 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)
  17. 29.

    29 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
  18. 30.
  19. 32.

    32 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
  20. 34.

    34 If I knew when mechanical ventilator patients were ready

    for a Spontaneous Breathing Trial (SBT) Mechanical Ventilation
  21. 35.

    35 I would reduce sedation and initiate an SBT trial

    earlier to decrease how much time patients spend in the ICU Mechanical Ventilation
  22. 43.

    43 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
  23. 45.

    45 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?
  24. 47.

    47 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
  25. 48.

    48 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
  26. 51.
  27. 53.

    53 When outcomes are uncertain, the best decision is the

    one that has the highest expected goodness.
  28. 54.

    54 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!
  29. 55.

    55 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!
  30. 56.

    56 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)
  31. 58.

    58 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
  32. 59.

    59 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