Cueing COVID-19: NLM Administrative Supplement for Research on Coronavirus Disease 2019

PROJECT SUMMARYThe variability and the complexity of the data needed for clinical care requires clinicians to accurately andefficiently recognize COVID-19 amongst individuals, ranging from asymptomatic infection to multiorgan andsystemic manifestations. COVID-19, like sepsis, involves different disease etiologies that span a wide range ofsyndromes (e.g., initial, inflammatory, hyperinflammatory response). Because patients can present with mild,moderate, or severe symptoms, clinicians must both identify the disease stage and optimal treatment. Thefactors that trigger severe illness in COVID-19 patients are not completely understood. Like other complex,challenging diagnoses, clinicians in the trenches struggle to diagnose and treat patients using data available inthe electronic health record (EHR). In our current NIH NLM R01 "Signaling Sepsis: Developing a Framework toOptimize Alert Design", we created sepsis specific enhanced visual display models that outranked preferenceand performance when compared with the usual care of fragmented, non-directed information gathering. For thissupplement, we propose the design and development of COVID-19 diagnosis and clinical managementenhanced visual display models to support clinicians' recognition of critical phases in COVID-19diagnosis and treatment decisions. In order to create the models, we will identify relevant diagnostic andtreatment data elements that will include clinical characteristics, laboratory results, and radiology results (e.g.,chest CT). Our project will survey emerging models of COVID-19 and its stages, and ensure our models arecongruent with best practices that emerge as our knowledge as a medical community evolves. The modelsprovide an EHR based method to mine clinical data to identify the presence of COVID-19 which supports thevariety of ways in which COVID-19 presents, availability of data elements, accuracy of diagnostic tests, and thehighly infective nature of the disease. Specific Aim 1: To identify emerging patient-specific clinical features ofCOVID-19 and testing analytics to present critical information for COVID-19 diagnosis and clinical management.Elements include the characteristics listed above (e.g., symptoms, co-morbidities) plus COVID-19 specific testresults, including data specific to the tests' positive and negative predictive values. Specific Aim 2: To developan EHR embedded CDS tool using our COVID-19 enhanced visual display models using synthesized informationobtained through the NLM parent grant and Specific Aim 1. Evaluate the technical feasibility and usability of thenovel COVID-19 CDS tool. Why It Matters: During a pandemic, there's no room for ambiguity as clinicians arerequired to comb through the EHR. The ability to better visualize and interpret EHR data supports optimaldiagnosis and clinical management. Our enhanced visual display models will support clinicians as they evaluatedemographic factors, underlying conditions, and comorbidities that identify patients at higher risk of morbidityand mortality and will therefore drive better clinical management.