Literature DB >> 33354320

An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments.

Muhammet Gul1, Erkan Celik1.   

Abstract

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.
© 2018 Operational Research Society.

Entities:  

Keywords:  Statistical forecasting; emergency departments; review

Year:  2018        PMID: 33354320      PMCID: PMC7738299          DOI: 10.1080/20476965.2018.1547348

Source DB:  PubMed          Journal:  Health Syst (Basingstoke)        ISSN: 2047-6965


  89 in total

1.  From model to forecasting: a multicenter study in emergency departments.

Authors:  Mathias Wargon; Enrique Casalino; Bertrand Guidet
Journal:  Acad Emerg Med       Date:  2010-09       Impact factor: 3.451

2.  Forecasting emergency department presentations.

Authors:  Robert Champion; Leigh D Kinsman; Geraldine A Lee; Kevin A Masman; Elizabeth A May; Terence M Mills; Michael D Taylor; Paulett R Thomas; Ruth J Williams
Journal:  Aust Health Rev       Date:  2007-02       Impact factor: 1.990

3.  Forecasting daily patient volumes in the emergency department.

Authors:  Spencer S Jones; Alun Thomas; R Scott Evans; Shari J Welch; Peter J Haug; Gregory L Snow
Journal:  Acad Emerg Med       Date:  2008-02       Impact factor: 3.451

4.  A retrospective analysis of the utility of an artificial neural network to predict ED volume.

Authors:  Nathan Benjamin Menke; Nicholas Caputo; Robert Fraser; Jordana Haber; Christopher Shields; Marie Nam Menke
Journal:  Am J Emerg Med       Date:  2014-03-19       Impact factor: 2.469

5.  An Interrupted Time-Series Analysis to Assess Impact of Introduction of Co-Payment on Emergency Room Visits in Cyprus.

Authors:  Panagiotis Petrou
Journal:  Appl Health Econ Health Policy       Date:  2015-10       Impact factor: 2.561

6.  Predicting emergency department inpatient admissions to improve same-day patient flow.

Authors:  Jordan S Peck; James C Benneyan; Deborah J Nightingale; Stephan A Gaehde
Journal:  Acad Emerg Med       Date:  2012-09       Impact factor: 3.451

7.  Emergency department crowding predicts admission length-of-stay but not mortality in a large health system.

Authors:  Stephen F Derose; Gelareh Z Gabayan; Vicki Y Chiu; Sau C Yiu; Benjamin C Sun
Journal:  Med Care       Date:  2014-07       Impact factor: 2.983

8.  A universal deep learning approach for modeling the flow of patients under different severities.

Authors:  Shancheng Jiang; Kwai-Sang Chin; Kwok L Tsui
Journal:  Comput Methods Programs Biomed       Date:  2017-11-07       Impact factor: 5.428

9.  A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

Authors:  Patrick Aboagye-Sarfo; Qun Mai; Frank M Sanfilippo; David B Preen; Louise M Stewart; Daniel M Fatovich
Journal:  J Biomed Inform       Date:  2015-07-04       Impact factor: 6.317

10.  Presentations to Emergency Departments for COPD: A Time Series Analysis.

Authors:  Rhonda J Rosychuk; Erik Youngson; Brian H Rowe
Journal:  Can Respir J       Date:  2016-04-04       Impact factor: 2.409

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  6 in total

1.  Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach.

Authors:  Jalmari Tuominen; Francesco Lomio; Niku Oksala; Ari Palomäki; Jaakko Peltonen; Heikki Huttunen; Antti Roine
Journal:  BMC Med Inform Decis Mak       Date:  2022-05-17       Impact factor: 3.298

2.  NO2 exposure increases eczema outpatient visits in Guangzhou, China: an indication for hospital management.

Authors:  Luwen Zhang; Dian Jing; Qiaochu Lu; Shuqun Shen
Journal:  BMC Public Health       Date:  2021-03-15       Impact factor: 3.295

3.  Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation.

Authors:  Bi Fan; Jiaxuan Peng; Hainan Guo; Haobin Gu; Kangkang Xu; Tingting Wu
Journal:  JMIR Med Inform       Date:  2022-07-20

4.  COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach.

Authors:  Dalton Borges; Mariá C V Nascimento
Journal:  Appl Soft Comput       Date:  2022-06-17       Impact factor: 8.263

5.  A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic.

Authors:  Egbe-Etu Etu; Leslie Monplaisir; Sara Masoud; Suzan Arslanturk; Joshua Emakhu; Imokhai Tenebe; Joseph B Miller; Tom Hagerman; Daniel Jourdan; Seth Krupp
Journal:  Healthcare (Basel)       Date:  2022-06-16

6.  Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.

Authors:  J Wolff; A Klimke; M Marschollek; T Kacprowski
Journal:  Sci Rep       Date:  2022-09-23       Impact factor: 4.996

  6 in total

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