Learn: COVID-19 Response
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Better Clinical Decision Support for COVID-19: Identifying Patients at Highest-Risk
Featured Content
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.
Insights
Better Clinical Decision Support for COVID-19: Identifying Patients at Highest-Risk
据估计，COVID-19患者的住院死亡率在15%-25%之间，因此必须尽早识别高风险个体。临床医生需要可靠的工具来识别严重恶化风险最高的个体。风险评分工具适用于常见的急性情况(如感染性疾病、低血容量或心源性休克)，但这些方法并不关注COVID-19的主要临床影响呼吸功能。因此，出现COVID-19严重症状的患者可能会根据心率和血压等生命体征表现稳定，但实际上他们处于危重状态。一种更先进的COVID-19风险评分方法侧重于低氧血症，或低于正常血氧水平。
Insights
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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