AI and crisis leadership: Using the POP-DOC Loop to explore potential implications and opportunities for leaders.

Autor: McNulty EJ; National Preparedness Leadership Initiative at Harvard University, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts. ORCID: https://orcid.org/0000-0003-0480-3516., Spisak BR; National Preparedness Leadership Initiative at Harvard University, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts., Marcus LJ; National Preparedness Leadership Initiative at Harvard University, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts., Cheema A; Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts., Dhawan R; Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts., Hertelendy A; Department of Information Systems and Business Analytics, College of Business, Florida International University, University Park, Florida., Novak S; Harvard Medical School, Cambridge, Massachusetts; The Canada International Scientific Exchange Program, Toronto, Ontario, Canada.
Jazyk: angličtina
Zdroj: Journal of emergency management (Weston, Mass.) [J Emerg Manag] 2024 Mar-Apr; Vol. 22 (2), pp. 119-127.
DOI: 10.5055/jem.0836
Abstrakt: In the evolving landscape of crisis leadership and emergency management, artificial intelligence (AI) emerges as a potentially transformative force with far-reaching implications. Utilizing the POP-DOC Loop, a comprehensive framework for crisis leadership analysis and decision-making, this paper delves into the diverse roles that AI is poised to play in shaping the future of crisis planning and response. The POP-DOC Loop serves as a structured methodology, encompassing key elements such as information gathering, contextual analysis informed by social determinants, enhanced predictive modeling, guided decision-making, strategic action implementation, and appropriate communication. Rather than offer definitive predictions, this review aims to catalyze exploration and discussion, equipping researchers and practitioners to anticipate future contingencies. The paper concludes by examining the limitations and challenges posed by AI within this specialized context.
Databáze: MEDLINE