Advancing Continuous Health Learning: NAM Digital Learning Collaborative AI Workgroup

 
NAM Digital Learning Collaborative
AI and the Future of Continuous Health Learning &
Improvement Workgroup – Publication Introduction
 
DLC AI & Future of Continuous Health Learning and
Improvement Workgroup
 
Original Charter: to explore the fields of AI and
their applications in health and health care
strategies to enhance data integration to advance
healthcare AI
practical challenges to AI model development and
implementation
opportunities for accelerating progress
 
Initial Workgroup Membership
 
Sonoo Thadaney, Stanford  (Workgroup Co-chair)
Michael Matheny, Vanderbilt (Workgroup co-chair)
John Burch, JLB Associates
Wendy Chapman, University of Utah
Jonathan Chen, Stanford University
Len D’Avolio, Cyft
Sharam Ebadollahi, IBM Watson Health Group
Hossien Estiri, Harvard Medical School
Steve Fihn, University of Washington
Jim Fackler, John Hopkins School of Medicine
Seth Hain, Epic
Brigham Hyde, Precision Health Intelligence
Edmund Jackson, HCA
Hongfang Liu, Mayo Clinic
Doug McNair, Cerner
Eneida Mendonca, University of Wisconsin Madison
Sean Khozin, FDA
Matthew Quinn, HRSA
Robert E. Samuel, Aetna
Bob Tavares, Emmi Solutions
Howard Underwood, Anthem)
Daniel Yang, Moore Foundation
 
 
 
 
Jonathan Perlin, CMO HCA, DLC Co-Chair
Reed Tuckson, Tuckson Health  Con., DLC Co-Chair
Wendy Nilsen, NSF
Joachim Roski, Booz Allen Hamilton
Howard Underwood, Anthem
Daniel Yang, Moore Foundation
Doug Badzik, Department of Defense)
Carlos Blanco, National Institute on Drug Abuse
Paul Bleicher, OptumLabs
Carla Brodley, Northeastern University
Tim Estes, Digital Reasoning
Daniel Fabbri, Vanderbilt University Medical Center
Kenneth R. Gersing, NIH
Michael Howell, Google
Brigham Hyde , Precision Health Intelligence
Javier Jimenez, Sanofi
Jennifer MacDonald, VA
Nigam H. Shah, Stanford)
David Sontag, MIT
Noel Southall, NIH
Shawn Wang, Anthem
Maryan Zirkle, PCORI
 
 
 
 
 
 
 
 
 
NAM Workgroup Publication Objectives & Scope
 
Develop a reference document for 
model
developers, clinical implementers, clinical
users, and regulatory and policy makers 
to:
understand strengths and limitations of AI/ML
promote use of these methods and technologies
within the healthcare system
Highlight areas of future work needed in research,
implementation science, and regulatory bodies to
facilitate broader use of AI/ML in healthcare
 
NAM DLC AI Publication: Organization
 
 
TOPIC
    
Leads
NAM DLC 
    
  Jonathan Perlin, Reed Tuckson
NAM Program Office
   
  Danielle Whicher, Mahnoor Ahmed
Publication Editors
   
  Sonoo Thadaney, Michael Matheny
 
Chapter 1: Introduction
   
  Sonoo Thadaney, Michael Matheny
Chapter 2: History of AI 
   
  Edmund Jackson, Jim Fackler
Chapter 3: Promise/Opportunities for AI 
 
  Joachim Roski, Wendy Chapman
Chapter 4: Pitfalls/Challenges for AI
 
  Eneida Mendonca, Jonathan Chen
Chapter 5: AI Development & Validation
 
  Hongfang Liu, Nigam Shah
Chapter 6: AI Deployment in Clinical Settings
 
  Steve Fihn, Andy Auerbach
Chapter 7: Regulatory & Policy Issues
 
  Doug McNair, Nicholson Price
Chapter 8: Conclusions & Key Needs
 
  Sonoo Thadaney, Michael Matheny
 
AI: What Do We Mean?
 
https://www.legaltechnology.com/latest-news/artificial-intelligence-in-law-the-state-of-play-in-2015/
 
“Health & Healthcare” Settings
 
Direct Encounter-Based Care
“Non-Traditional” Settings: CVS, Home
Population Health Management
“Back Office” Healthcare Administration
Patient/Consumer Facing Technologies
 
Target Audiences
 
Direct Care Providers
Patients and their Caregivers
Healthcare System Leadership & Admin
Data Scientists (Developers)
Clinical Informatics (Implementers)
Legislative & Regulatory Bodies
Third Party Payors
 
Chapter 2: History & Current State of AI
 
Discusses history of AI with examples
from other industries
Summarize the growth, maturity, and
adoption in healthcare as compared to
other industries.
Target general audience
 
Chapter 3: Promise & Potential Impact of AI
 
Focus on the utility of AI for improving
healthcare delivery
Discuss near-future opportunities and
potential gains from the use of AI
Target General Audiences
 
Chapter 4: Potential Unintended Consequences of AI
 
Focus on the potential unintended
consequences of AI on:
work processes
culture
equity / fairness
patient-provider relationship
workforce composition & skills
Target General Audiences
 
Chapter 5: AI Modeling Development & Validation
 
Most Technical Chapter
Topics
process for developing and validating models
choice of data, variables, model complexity
performance metrics, validation
 
Target Model Developers
 
Chapter 6: Deploying AI in Clinical Settings
 
Focus on implementing and maintaining AI
within ‘production’ healthcare domains
Address issues of:
Software development
Integration into a Learning Healthcare System
Applications of Implementation Science
Model Maintenance & Surveillance over Time
 
Target Healthcare System Leaders & Implementers
 
Chapter 7: Regulatory & Policy Considerations
 
Summarize key legislative and regulatory
considerations for the use of AI in health care
Identify strengths and weaknesses in current
framework
Discuss legal liability concerns
Make recommendations to address gaps
 
Chapter 8: Conclusions & Key Needs
 
build on and summarize key & cross-cutting
themes from previous chapters
 
Recommend key areas for:
Moving the field forward
Highlight over-arcing these from chapters
 
Publication Timeline
 
NAM Meeting 
    
11/2017
Publication Workgroup Kick-Off 
 
02/2018
Content Scope Established 
  
05/2018
Chapter Outlines Completed 
 
07/2018
Chapter Draft Versions 
  
09-12/2018
NAM Meeting 
    
01/2019
Publication Revisions
   
01-02/2019
NAM/External Reviews
  
03/2019
Tentative Release
   
04/2019
 
Mental Framework for This Meeting
 
Out of Scope: Discussion of Major Content
Additions/Subtractions
In Scope: Changes to Framing / Addressing Imbalance / Voice
of Chapters
In Scope: Focus on Recommendations
Identify and discuss modifications, additions, and subtractions as
each chapter is discussed
Be mindful of a desired balance between stakeholder groups
(patients, providers, administrators, regulatory bodies, etc.)
 
If you felt like the opportunity to discuss a point passed and
major themes, please send it to us in an email, or write it
down and give it to us during a break
mahmed@nas.edu
, 
dwhicher@nas.edu
 
Thank You
 
NAM Leadership
Victor Zhau
Michael McGinnis
DLC Leadership
Jonathan Perlin
Reed Tuckson
NAM Staff Leads
Danielle Whicher
Mahnoor Ahmed
DLC Clinical AI Workgroup Members
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Explore the intersection of AI and healthcare with the NAM Digital Learning Collaborative AI Workgroup, focusing on enhancing data integration, AI model development, and accelerating progress in continuous health learning. The workgroup aims to develop a reference document for stakeholders to understand and promote the use of AI/ML in healthcare, highlighting areas for further research and implementation.


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  1. NAM Digital Learning Collaborative AI and the Future of Continuous Health Learning & Improvement Workgroup Publication Introduction Michael E. Matheny, MD, MS, MPH Director, Center for Population Health Informatics Departments of Biomedical Informatics, Medicine, and Biostatistics Vanderbilt University Medical Center Twitter: @MichaelEMatheny Email: michael.Matheny@va.gov, michael.Matheny@Vanderbilt.edu

  2. DLC AI & Future of Continuous Health Learning and Improvement Workgroup Original Charter: to explore the fields of AI and their applications in health and health care strategies to enhance data integration to advance healthcare AI practical challenges to AI model development and implementation opportunities for accelerating progress

  3. Initial Workgroup Membership Sonoo Thadaney, Stanford (Workgroup Co-chair) Michael Matheny, Vanderbilt (Workgroup co-chair) John Burch, JLB Associates Wendy Chapman, University of Utah Jonathan Chen, Stanford University Len D Avolio, Cyft Sharam Ebadollahi, IBM Watson Health Group Hossien Estiri, Harvard Medical School Steve Fihn, University of Washington Jim Fackler, John Hopkins School of Medicine Seth Hain, Epic Brigham Hyde, Precision Health Intelligence Edmund Jackson, HCA Hongfang Liu, Mayo Clinic Doug McNair, Cerner Eneida Mendonca, University of Wisconsin Madison Sean Khozin, FDA Matthew Quinn, HRSA Robert E. Samuel, Aetna Bob Tavares, Emmi Solutions Howard Underwood, Anthem) Daniel Yang, Moore Foundation Jonathan Perlin, CMO HCA, DLC Co-Chair Reed Tuckson, Tuckson Health Con., DLC Co-Chair Wendy Nilsen, NSF Joachim Roski, Booz Allen Hamilton Howard Underwood, Anthem Daniel Yang, Moore Foundation Doug Badzik, Department of Defense) Carlos Blanco, National Institute on Drug Abuse Paul Bleicher, OptumLabs Carla Brodley, Northeastern University Tim Estes, Digital Reasoning Daniel Fabbri, Vanderbilt University Medical Center Kenneth R. Gersing, NIH Michael Howell, Google Brigham Hyde , Precision Health Intelligence Javier Jimenez, Sanofi Jennifer MacDonald, VA Nigam H. Shah, Stanford) David Sontag, MIT Noel Southall, NIH Shawn Wang, Anthem Maryan Zirkle, PCORI

  4. NAM Workgroup Publication Objectives & Scope Develop a reference document for model developers, clinical implementers, clinical users, and regulatory and policy makers to: understand strengths and limitations of AI/ML promote use of these methods and technologies within the healthcare system Highlight areas of future work needed in research, implementation science, and regulatory bodies to facilitate broader use of AI/ML in healthcare

  5. NAM DLC AI Publication: Organization TOPIC Leads Jonathan Perlin, Reed Tuckson Danielle Whicher, Mahnoor Ahmed Sonoo Thadaney, Michael Matheny NAM DLC NAM Program Office Publication Editors Chapter 1: Introduction Chapter 2: History of AI Chapter 3: Promise/Opportunities for AI Chapter 4: Pitfalls/Challenges for AI Chapter 5: AI Development & Validation Chapter 6: AI Deployment in Clinical Settings Steve Fihn, Andy Auerbach Chapter 7: Regulatory & Policy Issues Chapter 8: Conclusions & Key Needs Sonoo Thadaney, Michael Matheny Edmund Jackson, Jim Fackler Joachim Roski, Wendy Chapman Eneida Mendonca, Jonathan Chen Hongfang Liu, Nigam Shah Doug McNair, Nicholson Price Sonoo Thadaney, Michael Matheny

  6. AI: What Do We Mean? https://www.legaltechnology.com/latest-news/artificial-intelligence-in-law-the-state-of-play-in-2015/

  7. Health & Healthcare Settings Direct Encounter-Based Care Non-Traditional Settings: CVS, Home Population Health Management Back Office Healthcare Administration Patient/Consumer Facing Technologies

  8. Target Audiences Direct Care Providers Patients and their Caregivers Healthcare System Leadership & Admin Data Scientists (Developers) Clinical Informatics (Implementers) Legislative & Regulatory Bodies Third Party Payors

  9. Chapter 2: History & Current State of AI Discusses history of AI with examples from other industries Summarize the growth, maturity, and adoption in healthcare as compared to other industries. Target general audience

  10. Chapter 3: Promise & Potential Impact of AI Focus on the utility of AI for improving healthcare delivery Discuss near-future opportunities and potential gains from the use of AI Target General Audiences

  11. Chapter 4: Potential Unintended Consequences of AI Focus on the potential unintended consequences of AI on: work processes culture equity / fairness patient-provider relationship workforce composition & skills Target General Audiences

  12. Chapter 5: AI Modeling Development & Validation Most Technical Chapter Topics process for developing and validating models choice of data, variables, model complexity performance metrics, validation Target Model Developers

  13. Chapter 6: Deploying AI in Clinical Settings Focus on implementing and maintaining AI within production healthcare domains Address issues of: Software development Integration into a Learning Healthcare System Applications of Implementation Science Model Maintenance & Surveillance over Time Target Healthcare System Leaders & Implementers

  14. Chapter 7: Regulatory & Policy Considerations Summarize key legislative and regulatory considerations for the use of AI in health care Identify strengths and weaknesses in current framework Discuss legal liability concerns Make recommendations to address gaps

  15. Chapter 8: Conclusions & Key Needs build on and summarize key & cross-cutting themes from previous chapters Recommend key areas for: Moving the field forward Highlight over-arcing these from chapters

  16. Publication Timeline NAM Meeting Publication Workgroup Kick-Off 02/2018 Content Scope Established Chapter Outlines Completed Chapter Draft Versions NAM Meeting Publication Revisions NAM/External Reviews Tentative Release 11/2017 05/2018 07/2018 09-12/2018 01/2019 01-02/2019 03/2019 04/2019

  17. Mental Framework for This Meeting Out of Scope: Discussion of Major Content Additions/Subtractions In Scope: Changes to Framing / Addressing Imbalance / Voice of Chapters In Scope: Focus on Recommendations Identify and discuss modifications, additions, and subtractions as each chapter is discussed Be mindful of a desired balance between stakeholder groups (patients, providers, administrators, regulatory bodies, etc.) If you felt like the opportunity to discuss a point passed and major themes, please send it to us in an email, or write it down and give it to us during a break mahmed@nas.edu, dwhicher@nas.edu

  18. Thank You NAM Leadership Victor Zhau Michael McGinnis DLC Leadership Jonathan Perlin Reed Tuckson NAM Staff Leads Danielle Whicher Mahnoor Ahmed DLC Clinical AI Workgroup Members

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