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PLoS One ; 16(2): e0247244, 2021.
Article in English | MEDLINE | ID: covidwho-1105814

ABSTRACT

BACKGROUND: Emergency Department (ED) visits and health care costs are increasing globally, but little is known about contributing factors of ED resource consumption. This study aims to analyse and to predict the total ED resource consumption out of the patient and consultation characteristics in order to execute performance analysis and evaluate quality improvements. METHODS: Characteristics of ED visits of a large Swiss university hospital were summarized according to acute patient condition factors (e.g. chief complaint, resuscitation bay use, vital parameter deviations), chronic patient conditions (e.g. age, comorbidities, drug intake), and contextual factors (e.g. night-time admission). Univariable and multivariable linear regression analyses were conducted with the total ED resource consumption as the dependent variable. RESULTS: In total, 164,729 visits were included in the analysis. Physician resources accounted for the largest proportion (54.8%), followed by radiology (19.2%), and laboratory work-up (16.2%). In the multivariable final model, chief complaint had the highest impact on the total ED resource consumption, followed by resuscitation bay use and admission by ambulance. The impact of age group was small. The multivariable final model was validated (R2 of 0.54) and a scoring system was derived out of the predictors. CONCLUSIONS: More than half of the variation in total ED resource consumption can be predicted by our suggested model in the internal validation, but further studies are needed for external validation. The score developed can be used to calculate benchmarks of an ED and provides leaders in emergency care with a tool that allows them to evaluate resource decisions and to estimate effects of organizational changes.


Subject(s)
Emergency Medical Services/classification , Emergency Medical Services/economics , Emergency Service, Hospital/economics , Benchmarking , Health Care Costs , Health Care Surveys , Humans , Linear Models , Retrospective Studies , Switzerland , Universities
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