AI-Assisted Prioritisation

Chorus continuously evaluates incoming biosignal data to identify patients who may require clinical attention.

Machine learning models analyse multiple signal sources, including:

• ECG waveform patterns
• Blood pressure variability
• Blood glucose trends
• Oxygen saturation changes
• Weight and fluid retention indicators

Based on these analyses, the platform assigns risk indicators and surfaces patients in the registry according to clinical priority.

This enables providers to:

• Identify potential deterioration earlier
• Reduce time spent reviewing normal readings
• Focus on high-risk patients requiring intervention

AI prioritization assists clinicians but does not replace clinical judgment.