Real-Time Ventilator Monitoring for Earlier Clinical Intervention

Clinical scientists have developed a predictive model that detects when a ventilated patient requires airway suction significantly earlier than observation alone. This earlier intervention reduces discomfort for critically ill patients during their time in intensive care.

Using EMAP, we have built a data pipeline that records real-time output from ventilators and links it to individual patient records. This infrastructure enables predictive models like the one described above to run continuously, generating alerts for clinical staff when intervention is needed.

 

 

The storage requirements for continuous capture of high-resolution waveform data exceed the capacity of most research systems. However, when a research study requires this data, the SAFEHR team can activate the pipeline for a specified period and direct the captured data to the UCL Data Safe Haven, where it is ready for secure analysis while maintaining patient privacy and data governance standards.