Replay available

Data-driven patient safety initiatives represent a crucial step forward in healthcare. The possibility to analyse vast amounts of patient data in real-time facilitates early detection of adverse events or medical errors. Additionally, machine learning algorithms can be employed to predict patient outcomes and identify individuals at high risk for complications, enabling targeted interventions and personalised care plans.

To learn more about the power of data analytics in improving healthcare delivery, follow the webinar.

Aims & Objectives

• To understand the reliability and the potential of collected data
• To learn how to predict cardiovascular events in the ICU
• To describe the reliability of data in predicting failure of noninvasive oxygenation and ventilation strategies
  

Topics & Speakers 

Understanding the reliability and the potential of collected data
Christian JUNG
Heinrich-Heine-University, Dusseldorf (DE)

Predicting cardiovascular events in the ICU
Massimiliano GRECO
Humanitas University, Milan (IT)

Which data reliably predict failure of noninvasive oxygenation and ventilation strategies?
Lise Piquilloud
Vaud University Hospital Center, Lausanne (CH)