Abstract 12: Feasibility of Monitoring Covid-19 Related Stroke Patients Across a 14 Hospital Stroke System of Care
Background: Identifying and tracking COVID-19 related data has been crucial to the pandemic response. Most hospital systems have created internal tracking databases specific to COVID-19 but separated from other disease specific data pools. Traditional methods for tracking and trending novel and specific data such as COVID-19 related strokes may require personnel with highly technical skills to abstract the data. We aimed to create a COVID-19 stroke dashboard which would easily auto-abstract and update data. Methods: A simple monitoring system was designed using PowerBI™ and Microsoft Suite™ products that model existing data sources without using other IT resources. Existing data queries from various sources were modeled into one report and the resulting data model was used to track and trend incidence of COVID-19 and its relationship to stroke care throughout a 14- hospital stroke system. Results: The report allowed region-wide identification and evaluation of several metrics, including: volume of code strokes, the volume of patients who had a stroke within two weeks before or after testing positive for COVID-19, the initial NIHSS, if alteplase was administered, reason for no alteplase administration, delay in alteplase administration and if related to COVID-19 and the relationship of COVID-19 cases to the volume of code strokes. It was found that the volume of code strokes significantly decreased during the time of the pandemic and was inversely related to the volume of COVID-19 positive cases being reported in a county. The tool also found that COVID-19 positive stroke patients increased as the overall COVID-19 hospital volume increased. Conclusion: Assessing the relationships between a novel disease and other disease states may lead to changes in hospital workflows and practices resulting into improved patient outcomes.