Learn: COVID-19 Response

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Data Points to Respiratory Scoring

On-Demand Webinar
A Better Way: Data Points to Respiratory Scoring to More Accurately Predict Deterioration in COVID-19

Join Dr. Guy Glover, a critical care physician at Guy’s and St Thomas’ NHS Foundation Trust in the U.K., and Kathleen Merkley, Senior Vice President of Professional Services at Health Catalyst, as they discuss key findings from a U.K. study, in which Dr. Glover was the lead investigator.  The study assesses the widely used NEWS2 score versus a simple respiratory assessment, the Respiratory rate—Oxygenation (ROX) index, as a more sensitive predictor of deterioration among hospitalized patients with COVID-19. This will be set in a wider context of how to understand and optimize rapid response systems in hospitals for COVID-19 and beyond.

clinical decision support tools

Better Clinical Decision Support for COVID-19: Identifying Patients at Highest-Risk

Estimates place the in-hospital mortality for patients with COVID-19 between 15%-25%, making early identification of individuals at high risk as imperative. Clinicians need reliable tools to identify individuals at the highest risk of severe deterioration. Risk-scoring tools exist for common acute conditions (such as septic, hypovolemic, or cardiogenic shock), but these methods don’t focus on COVID-19’s primary clinical impact—respiratory function. As a result, patients experiencing severe symptoms of COVID-19 may appear stable according to vital signs, such as heart rate and blood pressure, when they’re in fact critically ill. A more evolved approach to COVID-19 risk scoring focuses hypoxemia, or a below-normal blood oxygen level.

moving average to quantify care disruption

Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

One of the biggest challenges health systems have faced since the onset of COVID-19 is the disruption to routine care. These care disruptions, such as halted routine checkups and primary care visits, place some patients at a higher risk for adverse outcomes. Health systems can rely on data science, based on past care disruption, to identify vulnerable patients and the short- and long-term effects these care disruptions could have on their health. Data science can also inform the care team which care disruptions to address first. With comprehensive information about care disruption on patients, health systems can apply the right interventions before it’s too late.

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