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Creating Value in Health Care through Big Data

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Notes

Recorded at the UTMB Health Policy Lecture Series, November 5, 2014. Joachim Roski, PhD, MPH, Principal, Booz Allen Hamilton offers a discussion of how healthcare is shifting and big data. Dr. Roski discusses the difference between big data and large data, and what has been found through working with organizations. 

Learning Module Notes Modules

  1. Introduction - How Health Care is shifting “thinking up big ideas” in health care reform to implementing them with big data (0:00 - 6:34)     
    • Asking provocative questions – how big data can help solve problems
    • What the aspiration of big data means for health care
  2. What is actually happening with data? (6:34-8:13) 
    • Electronic health care data will increase by a factor of 50 in the next 6 years - Personal monitoring devices (Fitbit, phones, etc)
  3. What is big data? - 3 V’s of Big Data: Volume, variety, velocity (8:13-10:19)
  4. What’s the difference between big and large data? (10:19-15:10) 
    • Data warehouse structures & info processing formats differ  “data lakes” replacing data warehouses
    • Data lakes won’t require transforming raw data – huge time saver, combo with tagging data with critical info
  5. Infrastructural Layers of Big Data (15:10-16:39)
  6. Differentiations of Big Data from other Research Methods: (16:39-21:39)
    • Collaboration of healthcare workers, mathematics & analytics, and computer science
    • May facilitate discovery across different scientific disciplines & explanatory frameworks
  7. How big data compares to other forms of analytics: (21:39-23:50)
    • Compared to advanced analytics, basic analytics, and big data computing
  8. Example: Use of Big Data in the VA (23:50-26:55)
  9. Example: Big Data in Improving Drug Manufacturing (26:55-29:01)
  10. Example: Big Data & the FDA approving medical devices & drugs (29:01-32:03)
  11. Example: Military Health System – Expensive, poor quality, unknown ways to improve care (32:03-36:31)
  12. Example: Medicare & fraudulent billing (36:31-38:40)
  13. What has been found through working with organizations with big data (38:40-44:09)
    • Find a problem
    • bring big data mindsets to that problem while keeping the problem small & confined to solve 
  14. Current Obstacles in Big Data (44:09-51:08)
    • Privacy, consent, data security, etc policies
  15. Questions (51:08-55:26 )
  Learning Modules