Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.

During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

Repeat virological and serological profiles in hospitalized patients initially tested by nasopharyngeal RT-PCR for SARS-CoV-2.

Real-time polymerase chain reaction (PCR) for SARS-CoV-2 is the mainstay of COVID-19 diagnosis, yet there are conflicting reports on its diagnostic performance. Wide ranges of false-negative PCR tests have been reported depending on clinical presentation, the timing of testing, specimens tested, testing method, and reference standard used. We aimed to estimate the frequency of discordance between initial nasopharyngeal (NP) PCR and repeat NP sampling PCR and serology in acutely ill patients admitted to the hospital. Panel diagnosis of COVID-19 infection is further utilized in discordance analysis. Included in the study were 160 patients initially tested by NP PCR with repeat NP sampling PCR and/or serology performed. The percent agreement between initial and repeat PCR was 96.7%, while the percent agreement between initial PCR and serology was 98.9%. There were 5 (3.1%) cases with discordance on repeat testing. After discordance analysis, 2 (1.4%) true cases tested negative on initial PCR. Using available diagnostic methods, discordance on repeat NP sampling PCR and/or serology is a rare occurrence.

Learning from the first wave: Lessons about managing patient flow and resource utilization on medical wards at providence health during the COVID-19 pandemic.

We report the successful implementation of a modified Traffic Control Bundling (TCB) protocol called "Red, Yellow and Green" on the inpatient medical units at St. Paul's Hospital in Vancouver, Canada during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. The modified TCB protocol demonstrates an important example on how hospitals can rapidly reorganize operational and clinical processes to reallocate existing capacity to minimize exposure, improve traffic flow and reduce nosocomial transmissions of COVID-19 to health care workers (HCWs) and other patients. Preliminary evidence demonstrates the benefits on how an existing facility can be redesigned for adjustable ward capacity to provide disease containment under a context of uncertainty of disease transmission and varying patient load. Important lessons in preparation for the evolution of the pandemic fall into categories of risk management, capacity and demand management.