Designing and Implementing Human-Centered AI: Comparing and Contrasting Approaches for Clinician and Patient Users

Artificial Intelligence (AI) has the potential to improve patient care and reduce clinician workload—but its success depends on how well it fits the real-world needs and workflows of patients and clinicians. This panel brings together experts in the design and implementation of human-centered AI across diverse healthcare settings, including emergency departments, outpatient care, and home- and community-based care. Panelists will compare and contrast methods for designing and implementing AI across contexts, users, and technologies. They will consider critical challenges such as supporting user understanding, enabling appropriate use, avoiding inequities in AI access and outcomes, and ensuring AI tools align with both clinician and patient goals. Attendees will gain practical insights into how human-centered methods can be applied to design and implement AI systems that improve care delivery while preserving clinician agency and advancing equity.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-09-01
DOI
https://doi.org/10.1177/10711813251369399
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Designing and Implementing Human-Centered AI: Comparing and Contrasting Approaches for Clinician and Patient Users

Natalie C. Benda, Nicole E. Werner, Enid N. H. Montague, Megan Elizabeth Salwei et al.
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Artificial Intelligence in Healthcare and Education
article

Designing and Implementing Human-Centered AI: Comparing and Contrasting Approaches for Clinician and Patient Users

Natalie C. Benda, Nicole E. Werner, Enid N. H. Montague, Megan Elizabeth Salwei, Erkin Ötleş, Hanna J. Barton
article en

Abstract

Artificial Intelligence (AI) has the potential to improve patient care and reduce clinician workload—but its success depends on how well it fits the real-world needs and workflows of patients and clinicians. This panel brings together experts in the design and implementation of human-centered AI across diverse healthcare settings, including emergency departments, outpatient care, and home- and community-based care. Panelists will compare and contrast methods for designing and implementing AI across contexts, users, and technologies. They will consider critical challenges such as supporting user understanding, enabling appropriate use, avoiding inequities in AI access and outcomes, and ensuring AI tools align with both clinician and patient goals. Attendees will gain practical insights into how human-centered methods can be applied to design and implement AI systems that improve care delivery while preserving clinician agency and advancing equity.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
University of Wisconsin–Madison (US), University of Toronto (CA), Columbia University Irving Medical Center (US), Vanderbilt University Medical Center (US)
Openalex Percentile: Top 83%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.