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1.
Crit Care Explor ; 2(6): e0136, 2020 Jun.
Artículo en Inglés | MEDLINE | ID: covidwho-1795100

RESUMEN

BACKGROUND: The current coronavirus disease 2019 pandemic is causing significant strain on ICUs worldwide. Initial and subsequent regional surges are expected to persist for months and potentially beyond. As a result of this, as well as the fact that ICU provider staffing throughout the United States currently operate at or near capacity, the risk for severe and augmented disruption in delivery of care is very real. Thus, there is a pressing need for proactive planning for ICU staffing augmentation, which can be implemented in response to a local surge in ICU volumes. METHODS: We provide a description of the design, dissemination, and implementation of an ICU surge provider staffing algorithm, focusing on physicians, advanced practice providers, and certified registered nurse anesthetists at a system-wide level. RESULTS: The protocol was designed and implemented by the University of Pittsburgh Medical Center's Integrated ICU Service Center and was rolled out to the entire health system, a 40-hospital system spanning Pennsylvania, New York, and Maryland. Surge staffing models were developed using this framework to assure that local needs were balanced with system resource supply, with rapid enhancement and expansion of tele-ICU capabilities. CONCLUSIONS: The ICU pandemic surge staffing algorithm, using a tiered-provider strategy, was able to be used by hospitals ranging from rural community to tertiary/quaternary academic medical centers and adapted to meet specific needs rapidly. The concepts and general steps described herein may serve as a framework for hospital and other hospital systems to maintain staffing preparedness in the face of any form of acute patient volume surge.

2.
Learn Health Syst ; 6(3): e10304, 2022 Jul.
Artículo en Inglés | MEDLINE | ID: covidwho-1653311

RESUMEN

Introduction: Rapid, continuous implementation of credible scientific findings and regulatory approvals is often slow in large, diverse health systems. The coronavirus disease 2019 (COVID-19) pandemic created a new threat to this common "slow to learn and adapt" model in healthcare. We describe how the University of Pittsburgh Medical Center (UPMC) committed to a rapid learning health system (LHS) model to respond to the COVID-19 pandemic. Methods: A treatment cohort study was conducted among 11 429 hospitalized patients (pediatric/adult) 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 (eg, 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: The mean (SD) daily number of admissions across hospitals was 26 (29) 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 odds ratio [OR] = 0.95, 95% confidence interval: 0.93-0.97, 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.

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