To Err Is AI: Who Bears the Blame for AI-Driven Clinical Decisions?

Presentation Time: 03:30 PM - 03:55 PM

Abstract Keywords: Artificial Intelligence, Legal, Ethical, Social and Regulatory Issues, Policy
Working Group: Nursing Informatics Working Group
Primary Track: Policy
Programmatic Theme: Clinical Informatics

The increasing adoption of artificial intelligence (AI) in nursing practice presents significant challenges related to liability, ethics, and regulatory oversight. AI-driven clinical decision support systems (CDSS) and large language models (LLMs) are transforming nursing workflows, yet legal frameworks have not kept pace with this shift.1 A key concern is whether nurses should be legally liable for AI-assisted clinical decisions, particularly given AI’s black box nature and limited transparency.2 This debate will explore the intersection of informatics, law, and professional accountability, assessing whether nurses should bear sole responsibility for AI-driven decisions or if the liability
should be distributed among AI vendors, institutions, and regulators.3

Moderator:
Meghan Reading Turchioe, PhD, MPH, RN
Columbia University School of Nursing

Speakers:
Benjamin Galatzan, PhD
University of Alabama Birmingham

Ann Wieben, PhD, BSN RN NI-BC
University of Wisconsin Madison

Carolyn Sun, PhD
Hunter College

Christina Baker, PhD,RN, NCSN, NI-BC
University of Colorado, Anschutz Medical Campus, College of Nursing

Authors:
Benjamin Galatzan, PhD - University of Alabama Birmingham; Ann Wieben, PhD, BSN RN NI-BC - University of Wisconsin Madison; Carolyn Sun, PhD - Hunter College; Meghan Reading Turchioe, PhD, MPH, RN - Columbia University School of Nursing; Christina Baker, PhD,RN, NCSN, NI-BC - University of Colorado, Anschutz Medical Campus, College of Nursing;

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Build or Buy: Finding the Network Partner(s) that Fit Best

Presentation Time: 03:55 PM - 04:20 PM

Abstract Keywords: Real-World Evidence Generation, Data Sharing, Governance, Legal, Ethical, Social and Regulatory Issues, Policy
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

The growth of large-scale, multi-institutional research into common practice make participation in research networks a practical requirement for healthcare institutions, whether academic or service-focused. There are a variety of network partners to choose from, including commercial organizations with a focus on therapeutic and regulatory research, and academic organizations with a focus on knowledge discovery and open science. In addition to research focus, these groups of networks differ in their technical infrastructure, requirements for sustainability, integration with data standards and other research initiatives, network of potential collaborators, and relationship to institutional mission. This debate will address the strengths of each approach, and factors that may affect decisions about participation in either or both class of research network to return value to the institution and pursuit of its mission.

Moderator:
Charles Bailey, MD, PhD
Children's Hospital of Philadelphia

Speakers:
Matvey Palchuk, MD, MS, FAMIA
TriNetX, LLC.

Phil Lindemann, .
Epic

Hanieh Razzaghi, PhD
Children's Hospital of Philadelphia

Harold Lehmann, MD, PhD
Johns Hopkins University

Authors:
Charles Bailey, MD, PhD - Children's Hospital of Philadelphia; Hanieh Razzaghi, PhD - Children's Hospital of Philadelphia; Harold Lehmann, MD, PhD - Johns Hopkins University; Matvey Palchuk, MD, MS, FAMIA - TriNetX, LLC.; Phil Lindemann, . - Epic;

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Balancing Control and Collaboration: The Merits of Federated, Centralized, and Hybrid Data Approaches in Medical Research

Presentation Time: 04:20 PM - 04:45 PM

Abstract Keywords: Data Mining, Privacy and Security, Usability
Primary Track: Policy
Programmatic Theme: Clinical Research Informatics

The collection and utilization of hospital data for research purposes is a critical aspect of advancing medical knowledge and improving patient care. In this panel, participants will debate three primary approaches to gathering hospital data: centralized data collection, federated data processing, and hybrid systems. The discussion will elucidate the advantages and disadvantages of each approach, providing a comprehensive understanding of their implications for research computing.

Moderator:
Shawn Murphy, MD, Ph.D.
Massachusetts General Hospital

Speakers:
Jeffrey Klann, PhD
Massachusetts General Hospital

Abu Mosa, PhD, MS, FAMIA
University of Missouri School of Medicine

Thomas McCoy, MD
Mass General Hospital

Authors:
Jeffrey Klann, PhD - Massachusetts General Hospital; Abu Mosa, PhD, MS, FAMIA; Thomas McCoy, MD - Mass General Hospital;

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