{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["18(12)"],"submitter":["Parnass G"],"pubmed_abstract":["Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding \"spikes\". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature."],"journal":["PloS one"],"pagination":["e0295130"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10691698"],"repository":["biostudies-literature"],"pubmed_title":["Estimating emergency department crowding with stochastic population models."],"pmcid":["PMC10691698"],"pubmed_authors":["Parnass G","Assaf M","Levtzion-Korach O","Peres R"],"additional_accession":[]},"is_claimable":false,"name":"Estimating emergency department crowding with stochastic population models.","description":"Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding \"spikes\". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-04-04T09:53:42.744Z","creation":"2025-02-19T04:23:58.052Z"},"accession":"S-EPMC10691698","cross_references":{"pubmed":["38039309"],"doi":["10.1371/journal.pone.0295130"]}}