2019
DOI: 10.1186/s12873-019-0256-z
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Real-time forecasting of emergency department arrivals using prehospital data

Abstract: Background Crowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED) arrivals on a short time-horizon incorporating data from prehospital services. Methods Time of notification and ED arrival was obtained for all… Show more

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Cited by 20 publications
(10 citation statements)
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“…A variety of studies have evaluated the factors influencing the demands for emergency medical service [ 12 ]. In particular, previous studies have reported the characteristics of patients visiting EDs and the number of patients according to seasons and weather conditions [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…A variety of studies have evaluated the factors influencing the demands for emergency medical service [ 12 ]. In particular, previous studies have reported the characteristics of patients visiting EDs and the number of patients according to seasons and weather conditions [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…Limited prehospital data have been used in developing ED prediction models to improve ED outcomes. Of the 12 studies reviewed that used prehospital data for ED decision-making; 1 study improved ED operations by forecasting number of arrivals to reduce overcrowding in ED 14 ; 4 studies predicted patient outcomes such as in-hospital mortality, survival rate, and return of spontaneous circulation (ROSC) 15 16 17 18 ; 2 studies identified specific risk or early warning scores for patient outcomes such as higher acuity or short-term in-hospital mortality 19 20 ; 2 studies made decisions in the field prior to arrival to ED such as triage patient disposition to specialized centers with appropriate medical capabilities (e.g., trauma centers or aortic surgery centers) 21 22 ; and 3 studies identified time-sensitive conditions. 21 22 23 All studies reported improvement in ED operations or ED outcomes as described in Table 1 .…”
Section: Resultsmentioning
confidence: 99%
“…For instance, a promising use of ML in ED using prehospital data could reduce overcrowding by managing the availability of personnel. 14 However, use of prehospital data are limited by the quality and availability of the data. Mashoufi et al conducted a survey among three groups of EMS stakeholders: data producers, data collectors, and data consumers.…”
Section: Discussionmentioning
confidence: 99%
“…In all health systems, the emergency structure represents an important link in the chain of patient care. However, the number of arrivals to emergencies continuously increased, and enormous organizational problems were caused and negatively influenced the quality of services (Asheim and al, 2019). To remedy the problems that have been caused by the massive influx of emergency room visits, we propose an approach founded on a data collection system.…”
Section: The Evolutionary Approachmentioning
confidence: 99%