摘要
ABSTRACT Introduction Rapid, continuous implementation of credible scientific findings and regulatory approvals is often slow in large, diverse health systems. The COVID-19 pandemic created a new threat to this common “slow to learn and adapt” model in healthcare. We describe how UPMC committed to a rapid learning health system (LHS) model to respond to the COVID-19 pandemic. Methods An observational cohort study was conducted among 11,429 hospitalized patients from 22 hospitals (PA, NY) with a primary diagnosis of COVID-19 infection (March 19, 2020 – June 6, 2021). Sociodemographic and clinical data were captured from UPMC electronic medical record (EMR) systems. Patients were grouped into four time-defined patient “waves” based on nadir of daily hospital admissions, with wave 3 (September 20, 2020 – March 10, 2021) split at its zenith due to high volume with steep acceleration and deceleration. Outcomes included changes in clinical practice (e.g., use of corticosteroids, antivirals, and other therapies) in relation to timing of internal system analyses, scientific publications, and regulatory approvals, along with 30-day rate of mortality over time. Results Mean (SD) daily number of hospital admissions was 26 (28) with a maximum 7-day moving average of 107 patients. System-wide implementation of the use of dexamethasone, remdesivir, and tocilizumab occurred within days of release of corresponding seminal publications and regulatory actions. After adjustment for differences in patient clinical profiles over time, each month of hospital admission was associated with an estimated 5% lower odds of 30-day mortality (adjusted OR = 0.95, 95% confidence interval: 0.92 – 0.97, p < .001). Conclusions In our large LHS, near real-time changes in clinical management of COVID-19 patients happened promptly as scientific publications and regulatory approvals occurred throughout the pandemic. Alongside these changes, patients with COVID-19 experienced lower adjusted 30-day mortality following hospital admission over time.