Evolution of Care and Outcomes Across Surges in Hospitalized Patients with Coronavirus Disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has unfolded in distinct surges. Understanding how surges differ may reveal important insights into the evolution of the pandemic and improve patient care. METHODS: We leveraged the Michigan Medicine COVID-19 Cohort, a prospective observational study at an academic tertiary medical center that systematically enrolled 2309 consecutive patients hospitalized for COVID-19, comprising 5 distinct surges. RESULTS: As the pandemic evolved, patients hospitalized for COVID-19 tended to have a lower burden of comorbidities and a lower inflammatory burden as measured by admission levels of C-reactive protein, ferritin, lactate dehydrogenase, and D-dimer. Use of hydroxychloroquine and azithromycin decreased substantially after Surge 1, while use of corticosteroids and remdesivir markedly increased (P < .001 for all). In-hospital mortality significantly decreased from 18.3% in Surge 1 to 5.3% in Surge 5 (P < .001). The need for mechanical ventilation significantly decreased from 42.5% in Surge 1 to 7.0% in Surge 5 (P < .001), while the need for renal replacement therapy decreased from 14.4% in Surge 1 to 2.3% in Surge 5 (P < .001). Differences in patient characteristics, treatments, and inflammatory markers accounted only partially for the differences in outcomes between surges. CONCLUSIONS: The COVID-19 pandemic has evolved significantly with respect to hospitalized patient populations and therapeutic approaches, and clinical outcomes have substantially improved. Hospitalization after the first surge was independently associated with improved outcomes, even after controlling for relevant clinical covariates.

COVID-19 Patient Under Investigation Self-Monitoring Mobile Application: Development and Implementation

This research aims to develop a mobile application for Covid-19’s Patient Under Investigation (PUI) Self-Monitoring (PUISM). The intended audience for this project is PUI cases, specifically (Covid-19) cases. The application was developed for PUI self-health monitoring in order to keep doctors, nurses, and administrators updated about their health status. As a method, the ADDIE model was used in this project. The ADDIE model was employed in this project as an approach. Five phases comprise the ADDIE method: Analysis, Design, Development, Implementation, And Evaluation. During the design phase, the developer will produce a prototype of mobile applications for PUI that will achieve the patient’s goals and patient requirements. The developer can then create mobile applications that follow the prototype’s design. During the implementation phase, the developer will publish the mobile application. The developer will then collect feedback, analyze the effectiveness of the mobile application for PUI, and determine whether the application is entirely functional or not. Finally, our endeavour benefits all frontlines by supporting them in tracking PUI that meet the criteria for Covid-19 occurrences. Additionally, this treatment reduces the transmission of infection Covid-19 to lessen the burden on the health care system and improve home self-monitoring.