Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

Betina Idnay, Zihan Xu, William G. Adams, Mohammad Adibuzzaman, Nicholas R. Anderson, Neil Bahroos, Douglas S. Bell, Cody Bumgardner, Thomas Campion, Mario Castro, James J. Cimino, I. Glenn Cohen, David Dorr, Peter L Elkin, Jungwei W. Fan, Todd Ferris, David J. Foran, David Hanauer, Mike Hogarth, Kun Huang, Jayashree Kalpathy-Cramer, Manoj Kandpal, Niranjan S. Karnik, Avnish Katoch, Albert M. Lai, Christophe G. Lambert, Lang Li, Christopher Lindsell, Jinze Liu, Zhiyong Lu, Yuan Luo, Peter McGarvey, Eneida A. Mendonca, Parsa Mirhaji, Shawn Murphy, John D. Osborne, Ioannis C. Paschalidis, Paul A. Harris, Fred Prior, Nicholas J. Shaheen, Nawar Shara, Ida Sim, Umberto Tachinardi, Lemuel R. Waitman, Rosalind J. Wright, Adrian H. Zai, Kai Zheng, Sandra Soo-Jin Lee, Bradley A. Malin, Karthik Natarajan, W. Nicholson Price, Rui Zhang, Yiye Zhang, Hua Xu, Jiang Bian, Chunhua Weng, Yifan Peng

TL;DR

This environmental scan investigates GenAI infrastructure across the CTSA network by surveying 36 CTSA leaders to map stakeholder roles, governance structures, and ethical oversight in GenAI adoption. It reveals centralized decision-making with cross-functional governance, variable ethical involvement, and broad experimentation, alongside substantial vendor engagement and a strong need for workforce training. The study highlights gaps in nurse and patient engagement and notes that most perceived benefits are operational rather than directly improving patient outcomes, underscoring the importance of robust governance, training, and evaluation. Overall, the work provides actionable guidance for national policy and institutional strategies to implement GenAI in healthcare ethically, equitably, and effectively.

Abstract

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

TL;DR

This environmental scan investigates GenAI infrastructure across the CTSA network by surveying 36 CTSA leaders to map stakeholder roles, governance structures, and ethical oversight in GenAI adoption. It reveals centralized decision-making with cross-functional governance, variable ethical involvement, and broad experimentation, alongside substantial vendor engagement and a strong need for workforce training. The study highlights gaps in nurse and patient engagement and notes that most perceived benefits are operational rather than directly improving patient outcomes, underscoring the importance of robust governance, training, and evaluation. Overall, the work provides actionable guidance for national policy and institutional strategies to implement GenAI in healthcare ethically, equitably, and effectively.

Abstract

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

Paper Structure

This paper contains 20 sections, 25 figures, 17 tables.

Table of Contents

  1. INTRODUCTION
  2. RESULTS
  3. Stakeholder Identification and Roles
  4. Decision-Making and Governance Structure
  5. Regulatory and Ethical Considerations
  6. Stage of Adoption
  7. Budget Trends
  8. Current LLM usage
  9. LLM Evaluation
  10. Projected Impact
  11. Enhancement Strategies
  12. Additional Insights into GenAI Integration
  13. DISCUSSION
  14. METHODS
  15. Study Design
  16. ...and 5 more sections

Figures (25)

  • Figure 1: Which stakeholder groups are involved in your organization's decision-making and implementation of GenAI?
  • Figure 2: Who leads the decision-making process for implementing GenAI applications in your organization?
  • Figure 3: How are decisions regarding adopting GenAI made in your healthcare institution?
  • Figure 4: Thematic analysis of governance and leadership structures in GenAI deployment across CTSA institutions with featured responses.
  • Figure 5: Which regulatory bodies are involved in overseeing the deployment of GenAI in your organization?
  • ...and 20 more figures