Dashboard for the neurosurgical intensive care unit of the University Hospital Zurich
In today’s intensive care units (ICUs), biosensors collect up to 100 GB of data per patient every day. These vast amounts of data are only valuable if they lead to better patient outcomes.
- How can medical staff gain insights from this data?
- How can risk constellations be identified as early as possible?
- How can therapeutic recommendations be adapted?
In the context of multiple research projects, our team is developing the "ICU Cockpit" user interface, a dashboard for the Neurosurgical intensive care unit of the University Hospital Zurich.
The dashboard visualizes patient data and makes algorithmic predictions accessible to ICU health professionals. It supports medical decisions and thereby improves patient safety.
Use cases
COVID-19 dashboard
In spring 2020, during the early phase of the COVID-19 pandemic, our team developed a "COVID-19 Dashboard" to display patient isolation requirements. The dashboard visualizes the arrangement of beds in the ICU, and displays the most recent SARS-CoV-2 testing/vaccination status for each patient. In addition, the user interface displays vital parameters such as heart rate or ECG, and allows for remote patient video surveillance.
COVID-19 diagnostics and prognostics
Current SARS-CoV-2 PCR tests have a rather high false-negative rate even after symptom onset. This is problematic for effectively ruling out virus transmission among patients and medical staff. Additionally, COVID-19 patients could greatly benefit from an early warning of worsening disease progression.
Based on routine biomarkers and high-resolution data from the ICU Cockpit, machine learning models can provide a more accurate diagnosis as well as a live prognosis of the course of disease.
Added to the ICU Cockpit “COVID-19 Dashboard”, they will support the medical staff in the ICUs by faster and more reliable recognition of SARS-CoV-2 infected patients and benefit severely ill patients through a more personalized therapy.
Prediction of Delayed Cerebral Ischemia (DCI)
Among subarachnoid hemorrhage (SAH) patients who survive a ruptured aneurysm, delayed cerebral ischemia (DCI) is the most important cause of poor neurological outcome or even death. Based on routine biomarkers and high-resolution data from the ICU Cockpit, machine learning models can provide accurate predictions of future DCI events. To support clinical practice in the ICU, algorithmic predictions must be accessible through a human-centered interface.
Media
Further Resources
- SNSF NRP 75 article: ICU cockpit: computer assistance for intensive and emergency medicine
- SNSF Data portal: From Tools to Teammates: Human-AI Teaming Success Factors in High-risk Industries
Project details
- Type
- Research project
- Research areas
- Medical decision support systems
- University
- FHNW School of Life Sciences / Institute for Medical Engineering and Medical Informatics
- Partner
- University Hospital Zurich
Contact
