The aim of this project is to test in a clinical trial a novel predictive early warning score for patients at risk of detection in the hospital using a SMARTapp on FHIR clinical decision support system across two large academic medical centers. Our CONCERN predictive model uses our healthcare process modeling approach to derive when a nurse is concerned about a patient from the patterns of EHR documentation. The model outperforms other early warning scores and has increased lead time - identifying at risk patients 5-24 hours earlier. We are extending and validating our CONCERN predictive model on the COVID-19 hospitalized population to provide clinical decision support for patients at risk of deterioration during a time of increased severity of illness and decreased resources. In addition we are leveraging our modeling approach and applying it to several resource modeling needs, including data-driven bed classification during the surge of creating new ICU beds and tracking ventilator status. These are challenging activities to track in the EHR, often requiring manual configuration and tracking.