I write therefore I am: note authoring, cognition, and generative AI (the existence of clinical reasoning in documentation)

Presentation Time: 08:30 AM - 09:00 AM

Abstract Keywords: Documentation Burden, Workflow, Legal, Ethical, Social and Regulatory Issues, Usability
Primary Track: Policy
Programmatic Theme: Clinical Informatics

Generative artificial intelligence (AI) is a tool that has been proposed to reduce documentation burden by synthesizing information and authoring narrative notes. Documentation burden is a critical issue. Applying generative AI to mitigate burdensome documentation tasks, such as creating the narrative of a clinical note, may be an important opportunity to address this issue. However, the act of creating a narrative note may support the development of clinical reasoning, aid decision-making, and increase situational awareness for clinicians. Delegating this cognitive task to generative AI may inadvertently degrade the development of clinical reasoning leading to new unintended consequences.

Moderator:
Jennifer Thate, PhD, CNE, RN
Siena College

Speaker(s):
Jennifer Thate, PhD, CNE, RN
Siena College

Deborah Levy, MD, MPH
Department of Veterans Affairs, VA-Connecticut Healthcare System / Yale School of Medicine

Elizabeth Sloss, PhD, MBA, RN
University of Utah

Elise Ruan, MD, MPH
Columbia University Department of Biomedical Informatics

Sarah Rossetti, RN, PhD
Columbia University Department of Biomedical Informatics

Author(s):

AI in EHRs will Reduce Documentation Burden Only with Appropriate Training

Presentation Time: 09:00 AM - 09:30 AM

Abstract Keywords: Documentation Burden, Education and Training, Large Language Models (LLMs)
Primary Track: Policy
Programmatic Theme: Clinical Informatics

Electronic Health Records (EHRs) and the increase in regulatory demands are linked with excessive documentation burden in clinical care. This burden increases the risk of health professionals (HPs) exiting the workforce and providing suboptimal patient care. Investment and deployment of Artificial Intelligence (AI) for patient care has reached an inflection point and is monotonically increasing. There is hope that AI may reduce documentation burden, but there is concern that regulatory demands will, again, increase with the rising use of AI in clinical care. Training in optimal use of EHR documentation tools is commonly offered as a solution to mitigate documentation burden, but there is debate as to the value of the opportunity cost of incremental EHR training. The debate will discuss whether documentation burden will be reduced given appropriate and comprehensive training in AI tools implemented within EHRs. The debate will be presented in a pro/con format.

Moderator:
Victoria Tiase, PhD, RN, NI-BC, FAMIA, FAAN, FNAP
University of Utah

Speaker(s):
Judy Murphy, DN, RN
IBM Global Healthcare

Rebecca Mishuris, MD, MS, MPH
Mass General Brigham

S. Trent Rosenbloom, MD, MPH, FACMI, FAMIA
Vanderbilt University Medical Center Dept of Biomedical Informatics

Sarah Corley, MD
MITRE Corporation

Author(s):

The necessity of fully informed consent prior to sharing clinical data for secondary uses

Presentation Time: 09:30 AM - 10:00 AM

Abstract Keywords: Data Sharing, Legal, Ethical, Social and Regulatory Issues, Privacy and Security, Patient Engagement and Preferences
Working Group: Ethical, Legal, and Social Issues Working Group
Primary Track: Policy
Programmatic Theme: Clinical Informatics

Hospitals, clinics, laboratories, and pharmacies routinely provide de-identified clinical data to 3rd parties for a wide range of research and commercial purposes. To the extent that hospitals seek consent for such data sharing, in most cases it is included within HIPAA acknowledgement forms and/or consent-to-treat forms that are typically signed at the time care is sought. On one hand, broad sharing of such data has great potential to support a range of useful applications. On the other hand, such sharing in the absence of true patient consent may violate basic principles of medical ethics, particularly in a technologic era where data sets can be easily re-identified.

Moderator:
Brian Jackson, MD, MS
University of Utah

Speaker(s):
Ross Koppel, PhD, FACMI, FIAHSI
University of Pennsylvania & University at Buffalo

Larry Ozeran, MD
Clinical Informatics, Inc.

Peter Elkin, MD, MACP, FACMI, FNYAM, FAMIA, FIAHSI
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York

Author(s):