Background: even though well-structured systems, trauma referrals often face challenges, resulting in patients not receiving timely care, adversely impacting outcomes. In this project addresses these issues by managing trauma patients and optimising the referral process.
Objectives: The primary objective is to analyse the current methods and philosophies behind trauma referrals and to identify tools for optimization. By dissecting the referral process using the Swiss Cheese Model as well as AI for a comprehensive literature review, we aim to enhance the effectiveness of trauma referrals.
Methods: We employed the Swiss Cheese Model to scrutinise each layer of the referral process, identifying potential points of failure. AI tools were utilised to perform a literature review, focusing on the intersection of linguistic and clinical studies. This involved using seeding articles and network visualisation to map out the complex interplay between multiple factors influencing trauma referrals.
Results: The analysis revealed multiple layers affecting trauma referrals , with numerous issues requiring detailed analysis . A significant finding was the dissociation between clinical and linguistic studies, each offering unique insights into the referral process. Connecting these disciplines could provide a more holistic understanding and lead to more effective optimization strategies.
Conclusions: There is a need to adopt a new perspective on trauma referrals. The Swiss Cheese Model offers a valuable framework for this analysis. Moreover , integrating linguistic and sociological analyses into the study of referral systems could uncover unique insights and might be improved patient outcomes. This multidisciplinary approach marks a necessary shift in how we optimise trauma referral processes.