Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2023-02, Vol.18 (2), p.e0282331
Main Authors: Garcia-Vicuña, Daniel, López-Cheda, Ana, Jácome, María Amalia, Mallor, Fermin
Format: Article
Language:English
Subjects:
Care and treatment
Censorship
Computer Simulation
Control
Coronaviruses
COVID-19
Critical care
Design of experiments
Epidemics
Equipment and Supplies, Hospital
Estimation
Hospital patients
Hospitalization
Hospitals
Humans
Intensive care
Intensive care units
Medicine and Health Sciences
Methods
Pandemics
Patients
Physical Sciences
Probability
Public health
Research and Analysis Methods
Services
Severe acute respiratory syndrome coronavirus 2
Simulation
Spain
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282331