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. 2025 Jul 1;25(1):106.
doi: 10.1186/s12873-025-01250-8.

Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda

Luka Petravić  1   2 , Kaja Gril Rogina  3   4 , Tit Albreht  3   4 , Andreja Kukec  3   4 , Janez Žibert  5
Affiliations

Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda

Luka Petravić et al. BMC Emerg Med. .

Abstract
in English, German

Background: Emergency departments (ED) are struggling with an increased influx of patients. One of the methods to help departments prepare for surges of admittance is time series forecasting (TSF). The aim of this study was to create an overview of current literature to help guide future research. Firstly, we aimed to identify external variables used. Secondly, we tried to identify TSF methods used and their performance.

Methods: We included model development or validation studies that were forecasting patient arrivals to the ED and used external variables. We included studies on any forecast horizon and any forecasting methodology. Literature search was done through PubMed, Scopus, Web of Science, CINAHL and Embase databases. We extracted data on methods and variables used. The study is reported according to TRIPOD-SRMA guidelines. The risk of bias was assessed using PROBAST and authors' own dimensions.

Results: We included 30 studies. Our analysis has identified 10 different groups of variables used in models. Weather and calendar variables were commonly used. We found 3 different families of TSF methods. However, none of the studies followed reporting guidelines and model code was seldom published.

Conclusions: Our results identify the need for better reported results of model development and validation to better understand the role of external variables used in created models, as well as for more uniform reporting of results between different research groups and external validation of created models. Based on our findings, we also suggest a future research agenda for this field.

Clinical trial number: Not applicable.

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Conflict of interest statement

Declarations. Ethical approval: IRB approval was not necessary for this type of study. Use of AI: Generative AI was not used in the creation of this manuscript or any parts of analysis. Patient and public involvement: No patients or public were involved in this study. Consent for publication: Not applicable Competing interest: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flowchart representing the study selection process
See this image and copyright information in PMC

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