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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...
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| Published in: | PloS one 2023-02, Vol.18 (2), p.e0282331 |
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| creator | Garcia-Vicuña, Daniel López-Cheda, Ana Jácome, María Amalia Mallor, Fermin |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0282331 |
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Application to bed demand prediction during pandemic waves</title><source>Open Access: PubMed Central</source><source>ProQuest - Publicly Available Content Database</source><source>Coronavirus Research Database</source><creator>Garcia-Vicuña, Daniel ; López-Cheda, Ana ; Jácome, María Amalia ; Mallor, Fermin</creator><contributor>Maqbool, Ayesha</contributor><creatorcontrib>Garcia-Vicuña, Daniel ; López-Cheda, Ana ; Jácome, María Amalia ; Mallor, Fermin ; Maqbool, Ayesha</creatorcontrib><description>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. 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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Garcia-Vicuña et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Garcia-Vicuña et al 2023 Garcia-Vicuña et al</rights><rights>2023 Garcia-Vicuña et al. 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| source | Open Access: PubMed Central; ProQuest - Publicly Available Content Database; Coronavirus Research Database |
| 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 |
| title | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves |
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