Cardiac arrest remains one of the most scrutinised and resource-intensive presentations in prehospital care. We invest heavily in improving what happens in the moment, compression quality, airway strategies, defibrillation timing, and team coordination. Yet one of the most powerful determinants of system performance often sits outside the resuscitation itself: what we do with the data afterwards.

In many services, once the patient is handed over or resuscitation is terminated, the process effectively stops. Information is fragmented, device data is reviewed inconsistently, and structured debriefing is applied variably. As a result, opportunities to identify system errors, reinforce high performance, and drive meaningful quality improvement are frequently lost.

At the same time, prehospital systems are generating more data than ever before CPR metrics, rhythm timelines, drug administration logs, and physiologic trends. The challenge is no longer collection, but conversion: turning raw resuscitation data into actionable learning. Layered on top of this is an emerging opportunity to deliver meaningful information to hospitals in real time, supporting earlier decision-making in the ED and cath lab before the patient even arrives.

In this episode, we explore how structured debriefing, post-arrest analysis, and improved data integration could transform cardiac arrest care from a series of isolated events into a continuous learning system spanning prehospital teams and receiving hospitals.

This episode is sponsored by PAX: The gold standard in emergency response bags.

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