Presentation provided at Northeastern University - School of Nursing

1. Translating Data into Clinical
Change
Hants Williams, PhD, RN
Founder & Chief Innovation Officer - BioVirtua
The Black Box of Data

2. Part 1 - Problem
Part 2 – Solutions
and Toolkit
Part 3 – Taking action
and Executing

3. My Journey
Data & Blackbox
Nurses

4. Birds eye view of my journey thus far…
Home Education Consulting
Founder

5. Duke University, PhD
Nonpharmacological pain management
Development of remote MBSR protocols
UMMC, Postdoc
Pain Genetics
RNA sequencing multidimensional pain
Parsley Health
Data Scientist
Customer insights, health outcomes
pulseData
Health Informaticist
Data translation, clinical recommendations
Robincare
Intervention consultant
Mindfulness-based interventions for cancer

7. Data ? Outcome

8. Data ML/Ai Outcome
“Data Scientist Stuff”

10. “Statistics is the science of learning from
data...” - American Statistical Association

11. Data ML/Ai Outcome

12. Data ML/Ai ? ⇧ Outcome
Translate into
action

13. Data ML/Ai OutcomeNurses

14. Mathematical Models  Translation Improved Health
Basic Science  Translational Science  Clinical Science

15. Data
Problem
ML/Ai ModelModel Translation
Intervention with
Metric

16. Real world example:
Background: pulseData, preventable chronic diseases with machine
learning – chronic kidney disease
Problem: exploring partnership with Oscar health, how could they
integrate, provide additional value
Solution:

19. High Level Game Plane
1. Literature Review [pubmed, ebscho, uptodate]– find evidenced based
interventions to support the implementation of some type of
intervention (most likely nurse, PT, social worker led)
2. Human Capital - hospital/client resources (e.g., social workers, nurse
specialists) that are already in place
3. Understand output from ML/Ai specialists – what it can and can’t do
4. Pairing of human capital resources to EBP’s

20. Do nurses need
to become
experts in ML/Ai?
No
Do nurses need
to understand
basics of ML/Ai?
Yes

21. Data scientists are not trained in:
• Anatomy
• Disease/pathology
• Pharmacogenetics
• Acute Health
• Hospital Operations/Management

22. Data scientists’ are
mathematicians/visualization
experts that should be agnostic
to the context or scenario
Can create ML/Ai models, but not
healthcare professionals, and are
not trained to translate their
findings into improved patient
outcomes
Nurses’ are focused on improving
patient reported outcomes/metrics
through delivery of evidenced-based
care
Nurses are trained and
knowledgeable of healthcare
interventions that could be
delivered, and what may be most
effective

23. Famous Failures and Problems
IBM Watson – University Texas
“The partnership between IBM and one of the world’s
top cancer research institutions is falling apart. The
project is on hold, MD Anderson confirms, and has
been since late last year. MD Anderson is actively
requesting bids from other contractors who might
replace IBM in future efforts. And a scathing report
from auditors at the University of Texas says the
project cost MD Anderson more than $62 million and
yet did not meet its goals. The report, however, states:
‘Results stated herein should not be interpreted as an
opinion on the scientific basis or functional capabilities
of the system in its current state’….”

24. https://www.technologyreview.com/s/607965/a-reality-check-for-ibms-ai-ambitions/

25. Are there opportunities?

26. • pulseData --> They create risk scores for preventable illnesses, but
what do they do with those scores?
• Parsley Health --> They provide adjunct doctor visits and health
coaching interactions, but lack evidenced-based protocols for coaches
• Robincare  Using non-pharmacological approaches for managing
cancer in young adults, but with approaches that are not matched
with the condition (e.g., generic use of mindfulness)
Omada Care (prediabetes)
Vita Health (weight management)
Flatiron (cancer)

27. The best part of this ‘blackbox’ - you don’t need to
be a data scientist, you need to be a nurse with
only a basic understanding of what is under the
hood

28. “…provide input to the overall NIH vision and actions undertaken by
each of the 27 Institutes and Centers in support of biomedical research
as a digital enterprise. Among other duties, the office oversees the Big
Data to Knowledge (BD2K) initiative, stimulating the best developments
in the data science community.”
Patricia Flatley Brennan, RN, PhD
NIH Interim Associate Director for Data Science
National Library of Medicine Director

30. My Toolbox (“stack”)
How I Utilize Free & Open Source Tools
to…
1. Find opportunities (e.g., clients, projects)
2. Learning basic ‘data science’ – a.k.a how to make pretty graphs and do
statistics
3. Attract and sell my skillset
Discloser: I am receiving no monetary or other forms of compensations from
any of the services I am about to discuss

31. Findings Opportunities and Clients

32. Data Specific Tools to Solve The Problems
Interact w/data
Example
code
Visualize &
Report

33. Getting The Word Out – my skills

34. Wordpress and the Divi Theme – Getting the Word Out

35. Wordpress and the Divi Theme – Getting the Word Out

41. Where’s the value?

44. Alternatives to VC money-

45. My contact:
Personal email: hantsawilliams@gmail.com
650-218-3789
Schedule some time to chat with me @
https://calendly.com/hants