ABSTRACT
The SARS-CoV2 pandemic and high hospitalization rates placed a tremendous strain on hospital resources necessitating models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV2 2 in New York (October 2020-April 2021) for its accuracy in predicting number of COVID-19 admissions at three, five, seven and 10 days into the future comparing predicted admissions with actual admissions for each day at a large integrated healthcare delivery network. Mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for five-day predictions, 12.4%-13.2% for seven-day predictions, and 17.1-17.8% for 10-day predictions).
ABSTRACT
Within weeks of New York State's first confirmed case of COVID-19, New York City became the epicenter of the nation's COVID-19 pandemic. With more than 80,000 COVID-19 hospitalizations during the first wave alone, hospitals in downstate New York were forced to adapt existing procedures to manage the surge and care for patients facing a novel disease. Given the unprecedented surge, effective patient load balancing-moving patients from a hospital with diminishing capacity to another hospital within the same health system with relatively greater capacity-became chief among the capabilities required of New York health systems. The Greater New York Hospital Association invited members of downstate New York's 6 largest health systems to talk about how each of their systems evolved their patient load balancing procedures throughout the pandemic. Informed by their insights, experiences, lessons learned, and collaboration, we collectively present a set of consensus recommendations and best practices for patient load balancing at the facility and health system level, which may inform regional approaches to patient load balancing.
ABSTRACT
The suspension of in-person classes brought about by the COVID-19 pandemic necessitated adjustments to all manner of instruction. Laboratory instruction was particularly challenged since the primary response was to deliver content remotely, i.e., online. In an effort to reinforce laboratory topics and engage students, five click-through organic chemistry lab stories modeled after the Choose your Own Adventure book series were created. These labventures were (1) use of FT-IR;(2) identification by mixed melting point;(3) liquidliquid extraction;(4) recrystallization;and (5) chemical reaction. To facilitate assessment, each story was deployed on the Learning Management System so the number of times students visit each page of the story could be counted. These assignments were generally well received by students and revealed varied levels of understanding of the laboratory material.
ABSTRACT
INTRODUCTION: Neurologic symptoms present as significant complications of coronavirus disease 2019 (COVID-19) infection. This report describes a novel manifestation of tremors triggered by severe acute respiratory syndrome coronavirus 2 infection. CASE PRESENTATION: We describe a case of a 46-year-old man with COVID-19 infection complicated by a bilateral intention tremor and wide-based gait. Although neurological manifestations have been reported related to COVID-19, tremulousness has not yet been described. CONCLUSION: Considering the evolving diversity of neurologic manifestations in this infection, emergency physicians should be vigilant of possible COVID-19 infection in patients presenting with unexplained neurologic symptoms.