Machine Learning for Enhancing Quality and Safety in Trauma Care for Older Adults: A Conceptual Framework

Manuscript submitted August 25, 2026; accepted September 22, 2026; published September 29, 2026.

Abstract—Older adults face disproportionately high risks of Adverse Events (AEs) following trauma, including surgical complications, delirium, medication errors, and infections. As trauma care systems globally confront aging populations, Machine Learning (ML) offers significant potential to enhance safety and quality across all phases of care. This article proposes a conceptual framework that maps ML maturity onto the trauma care continuum to inform future research and implementation strategies. We review the current landscape of ML applications focused on older trauma patients, examine national data on age-specific AE trends and categories, and identify key challenges, such as data fragmentation, model bias, and limited clinical integration. Finally, we outline future priorities to support the safe, equitable, and effective use of ML in trauma care for older adults.

keywords—trauma care, care quality, patient harm, machine learning, conceptual framework

Cite: Guosong Wu,"Machine Learning for Enhancing Quality and Safety in Trauma Care for Older Adults: A Conceptual Framework," Journal of Advances in Artificial Intelligence, vol. 4, no. 3, pp. 190-199, 2026. doi: 10.18178/JAAI.2026.4.3.190-199

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).