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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0282331 |
| _version_ | 1797848122743848960 |
|---|---|
| author | Daniel Garcia-Vicuña Ana López-Cheda María Amalia Jácome Fermin Mallor |
| author_facet | Daniel Garcia-Vicuña Ana López-Cheda María Amalia Jácome Fermin Mallor |
| author_sort | Daniel Garcia-Vicuña |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T18:22:24Z |
| format | Article |
| id | doaj.art-a4acbeebd61f46838b1d4a3bcda437a8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T18:22:24Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a4acbeebd61f46838b1d4a3bcda437a82023-04-12T05:33:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01182e028233110.1371/journal.pone.0282331Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.Daniel Garcia-VicuñaAna López-ChedaMaría Amalia JácomeFermin MallorHospital 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.https://doi.org/10.1371/journal.pone.0282331 |
| spellingShingle | Daniel Garcia-Vicuña Ana López-Cheda María Amalia Jácome Fermin Mallor Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. PLoS ONE |
| title | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. |
| title_full | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. |
| title_fullStr | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. |
| title_full_unstemmed | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. |
| title_short | Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves. |
| title_sort | estimation of patient flow in hospitals using up to date data application to bed demand prediction during pandemic waves |
| url | https://doi.org/10.1371/journal.pone.0282331 |
| work_keys_str_mv | AT danielgarciavicuna estimationofpatientflowinhospitalsusinguptodatedataapplicationtobeddemandpredictionduringpandemicwaves AT analopezcheda estimationofpatientflowinhospitalsusinguptodatedataapplicationtobeddemandpredictionduringpandemicwaves AT mariaamaliajacome estimationofpatientflowinhospitalsusinguptodatedataapplicationtobeddemandpredictionduringpandemicwaves AT ferminmallor estimationofpatientflowinhospitalsusinguptodatedataapplicationtobeddemandpredictionduringpandemicwaves |