Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning

• Published in: BMC infectious diseases Vol. 21; no. 1; pp. 700 - 15
• Main Authors: Vekaria, Bindu, Overton, Christopher, Wiśniowski, Arkadiusz, Ahmad, Shazaad, Aparicio-Castro, Andrea, Curran-Sebastian, Jacob, Eddleston, Jane, Hanley, Neil A, House, Thomas, Kim, Jihye, Olsen, Wendy, Pampaka, Maria, Pellis, Lorenzo, Ruiz, Diego Perez, Schofield, John, Shryane, Nick, Elliot, Mark J.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 22.07.2021; BioMed Central; BMC
• Subjects: Aged; Bias; Care and treatment; Clinical outcomes; Complexity; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - therapy; Critical care; Data Analysis; Data collection; England; England - epidemiology; Epidemics; Estimates; Failure times; Female; Hospital Bed Capacity; Hospital patients; Hospital Planning - methods; Hospital utilization; Humans; Infections; Infectious diseases; Intensive Care Units - statistics & numerical data; Length of stay; Length of Stay - statistics & numerical data; Male; Medical informatics; Methods; Middle Aged; Occupancy; Pandemics; Patients; Quarantine; Severe acute respiratory syndrome coronavirus 2; Statistics; Survival; Survival Analysis; Viral diseases; Weibull distribution; England; United Kingdom; United Kingdom > UK; COVID-19; England; Survival Analysis; Length of stay

Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting Lo...