Context:

Our healthcare client was conducting a clinical trial to proactively identify ICU patients at risk of deterioration and reduce adverse outcomes like sepsis, respiratory failure, or cardiac arrest.

Resolution:

To solve this, a simple AI tool was developed by our specialized team implementing a predictive analytics platform to which data sources of real-time feeds from electronic health records (EHRs), vital signs monitors, and other clinical systems were used. Also, Machine learning algorithms were trained on historical patient data to detect early warning signs. Finally, alerts were embedded into clinical workflows to notify providers of potential deterioration.

Result:

The AI tool reduced ICU mortality rates, shortened the length of stay in hospitals, improved patient satisfaction and increased clinical responses.