Human-Centered AI for Nursing Workload Optimization: Advancing Clinical Quality and Reducing Burnout
Other Titles
Human-Centered AI for Nursing Workload Optimization [Poster Title]
Abstract
Nursing professionals face increasing administrative and cognitive burdens that contribute to fatigue, burnout, and compromised patient safety. Despite the proliferation of electronic health records (EHRs), current systems often amplify rather than alleviate workload complexity. This study, developed under the AIM-AHEAD CLINAQ Fellowship, explores a human-centered artificial intelligence (AI) framework to optimize nursing workload distribution while promoting clinical equity and efficiency.
The project applies design-thinking methodology and interdisciplinary collaboration across informatics, data science, and clinical practice. Using EHR-derived activity logs and observational data, a predictive model was developed to identify high-intensity workflow patterns and recommend task reallocation strategies. The algorithm integrates principles of algorithmic transparency and ethical governance to ensure fairness and interpretability across diverse care environments.
Preliminary validation with simulated data indicates reduced redundant documentation time and improved alignment between nurses' skill sets and patient acuity levels. Anticipated outcomes include decreased burnout indicators, enhanced patient throughput, and improved satisfaction among both staff and patients.
This research highlights the need for proactive AI design that aligns with the cognitive and emotional realities of nursing work. Human-centered informatics emphasizes that technology should augment rather than replace professional judgment. By embedding ethical oversight, continuous feedback, and user-driven iteration into the AI lifecycle, healthcare systems can achieve a sustainable balance between efficiency and compassion.
The framework proposed offers an actionable model for integrating responsible AI into clinical quality initiatives. Future phases will involve multi-site pilot testing across varied healthcare settings to evaluate scalability, interoperability, and measurable impact on nurse well-being and patient outcomes.
Notes
References:
Dall’Ora C, et al. “Burnout in Nursing: A Theoretical Review.” Int J Nurs Stud. 2020; 107: 103524.
Rajkomar A, et al. “Machine Learning in Medicine.” N Engl J Med. 2019; 380(14): 1347-1358.
Collier A. (2025). “Biotechnology and IT Governance: Ethical and Security Implications for IT Professionals.” ISACA Journal, 3.
Sigma Membership
Non-member
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Implementation Science, Workforce, Burnout, Interdisciplinary Collaboration, Documentation, Collaboration
Recommended Citation
Collier, Alexis, "Human-Centered AI for Nursing Workload Optimization: Advancing Clinical Quality and Reducing Burnout" (2026). International Nursing Research Congress (INRC). 30.
https://www.sigmarepository.org/inrc/2026/posters_2026/30
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-26
Human-Centered AI for Nursing Workload Optimization: Advancing Clinical Quality and Reducing Burnout
Toronto, Ontario, Canada
Nursing professionals face increasing administrative and cognitive burdens that contribute to fatigue, burnout, and compromised patient safety. Despite the proliferation of electronic health records (EHRs), current systems often amplify rather than alleviate workload complexity. This study, developed under the AIM-AHEAD CLINAQ Fellowship, explores a human-centered artificial intelligence (AI) framework to optimize nursing workload distribution while promoting clinical equity and efficiency.
The project applies design-thinking methodology and interdisciplinary collaboration across informatics, data science, and clinical practice. Using EHR-derived activity logs and observational data, a predictive model was developed to identify high-intensity workflow patterns and recommend task reallocation strategies. The algorithm integrates principles of algorithmic transparency and ethical governance to ensure fairness and interpretability across diverse care environments.
Preliminary validation with simulated data indicates reduced redundant documentation time and improved alignment between nurses' skill sets and patient acuity levels. Anticipated outcomes include decreased burnout indicators, enhanced patient throughput, and improved satisfaction among both staff and patients.
This research highlights the need for proactive AI design that aligns with the cognitive and emotional realities of nursing work. Human-centered informatics emphasizes that technology should augment rather than replace professional judgment. By embedding ethical oversight, continuous feedback, and user-driven iteration into the AI lifecycle, healthcare systems can achieve a sustainable balance between efficiency and compassion.
The framework proposed offers an actionable model for integrating responsible AI into clinical quality initiatives. Future phases will involve multi-site pilot testing across varied healthcare settings to evaluate scalability, interoperability, and measurable impact on nurse well-being and patient outcomes.
Description
This study presents a human-centered AI framework for optimizing nursing workloads through EHR-integrated predictive modeling. The approach reduces burnout, enhances patient safety, and embeds algorithmic ethics into healthcare quality improvement.
Acknowledgments: NIH AIM-AHEAD Initiative (Agreement No. 1OT2OD032581); AIM-AHEAD CLINAQ Fellowship Program at Morehouse School of Medicine. IP disclosure: thirteen U.S. provisional patent applications filed December 2025 through April 2026. Disclosure: Dr. Collier is Founder and CEO of VitaSignal LLC, a commercial entity developing DBS and related frameworks. Pre-market research prototype. Decision support only. Not FDA cleared or approved. Not for clinical use. |