Hospitals face challenges in accurately and promptly detecting conditions like sepsis, in-hospital mortality risk, and cardiac decompensation, leading to delayed interventions and potentially adverse patient outcomes. Traditional rule-based alert systems often generate false positives, contributing to alarm fatigue and hindering effective clinical decision-making.

Cohere Med offers a clinical decision support platform that leverages data science and machine learning to provide augmented clinical analytics, improving the accuracy and speed of critical condition detection. The platform analyzes real-time EHR data, including patient vitals, laboratory results, and medical administrations, to identify patients at risk of sepsis, predict in-hospital mortality, and detect cardiac decompensation early. By incorporating a time-series of clinical data since a patient's admission, the platform accounts for patient progression and provides actionable insights. This approach reduces false positives and enables clinicians to make informed decisions, leading to increased bundle compliance, reduced length of stay, and lower mortality rates.

Real-time monitoring of patient data from admission to discharge for early detection of critical conditions

Machine learning models trained on extensive datasets, including millions of clinical parameters from over 42,000 patients

Algorithms that capture patient progression by analyzing time-series data

Specific solutions for:

Sepsis Watch: Early and accurate sepsis detection with timely care coordination

Mortality Model: Prediction of in-hospital mortality to improve clinical decisions

Cardiac Decompensation: Early identification of high-risk cardiac decompensation phenotypes

Integration with existing EHR systems

The primary target audience includes hospitals and health systems seeking to improve clinical outcomes, reduce costs, and enhance operational efficiency in acute care settings.