Gynecological Emergencies in Urgent Care Clinics: Triage and Optimized Flow

This research addresses the optimization of triage and management in gynecological emergencies in emergency outpatient clinics, focusing on the integrated application of process modeling, Bayesian analysis, and artificial intelligence. It is contextualized in light of the growing demand for agile and accurate care, in the face of operational and clinical challenges that impact the quality and safety of care. The problem focuses on the need to improve the efficiency of care flow and clinical decision-making in highly complex and urgent environments. The general objective was to investigate and validate innovative methods to optimize processes and support decisions in gynecological emergencies, promoting better clinical and organizational results. To this end, the Giftedean neoperspectivist paradigm was adopted, incorporating theories such as Complexity Theory, Decision Theory, and Collaborative Management Theory. The hypothetical-deductive method was used in its stages, complemented by a rigorous narrative bibliographic and documentary review, carried out in the PubMed, Scopus and Web of Science databases, with the descriptors “gynecological emergency”, “optimized triage”, “Bayesian analysis”, “artificial intelligence”, resulting in the analysis of 75 relevant works. The main findings indicate that the combination of the adopted methodologies promoted a significant reduction in service times, greater diagnostic accuracy and strengthening of multidisciplinary collaboration. It was concluded that these approaches are effective in improving care in gynecological emergencies, although there are gaps in terms of adaptation in contexts with limited resources and the need for more intuitive interfaces. Limitations include the predominant use of retrospective data and restricted samples. The contributions cover theoretical, methodological and practical advances, adding value to maternal health, postgraduate studies and society in general.