Effective communication in emergency medical services is critical in high-stakes scenarios, where information must be conveyed with speed, precision, and clarity. However, background noise, stress-induced speech patterns, and the use of specialized medical terminology frequently hinder comprehension. Improving the reliability of emergency communication is therefore a pressing challenge for both clinical outcomes and operational efficiency. This paper introduces a robust multi-model information extraction pipeline designed to enhance the accuracy and efficiency of emergency medical communication. The pipeline integrates advanced Speech-to-Text (STT) systems with Large Language Models (LLMs) to both improve transcription fidelity and extract mission-critical medical data. It comprises four modules: (1) audio capture of simulated German emergency communications under varied acoustic conditions, (2) STT transcription using Whisper, Azure, and IBM Watson, (3) LLM-driven refinement of transcriptions with GPT-4 to correct grammatical and terminological errors, and (4) structured information extraction with GPT-4, LLaMA 3.2, and Mixtral-8, guided by Chain-of-Thought and role-based prompting. The whole pipeline is evaluated using Word Error Rate (WER), BLEU, ROUGE-L, and semantic similarity, alongside accuracy, completeness, and relevance of extracted data. Azure STT with GPT-4 proved optimal, achieving the lowest post-refinement WER (0.1812, a 32.7% improvement), and high semantic similarity (0.9736), ROUGE-L (0.8802), and BLEU (0.7457). GPT-4 reached near-perfect extraction accuracy (0.995), surpassing LLaMA 3.2 and Mixtral-8, though Mixtral-8 remained highly competitive (0.980 accuracy with Whisper). Overall, the proposed pipeline demonstrates how combining STT and LLMs can transform noisy emergency dialogues into precise, structured clinical data, advancing responsive and reliable emergency management systems.