KaydedilenlerDerginizi ekleyin

Dergiler / Eurasian Journal of Emergency Medicine / 2022 / Cilt: 21 - Sayı: 1

Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments

Mustafa Gökalp ATAMAN
Görkem SARIYER
Sayfa
34–44
Yayın Tarihi
2022-10-11
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Conclusion: By emphasizing the impact of mode of arrival in ED context, this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes.
📊
Temel Bulgular & Veri:The mean absolute percentage errors (MAPE) for the models of four time series are 5,432%, 13,085%, 9,955% and 10.984%, respectively.
💡
Sonuç & Katkı:Conclusion: By emphasizing the impact of mode of arrival in ED context, this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this study, developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning of ED operations. Materials and Methods: In this study, by categorizing the mode of arrival into two, self-arrived in and by ambulance, autoregressive integrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an ambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a large-scaled urban ED in İzmir, Turkey. Results: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes, non-seasonal models are proper for forecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5,432%, 13,085%, 9,955% and 10.984%, respectively. Thus, daily arrivals to the EDs show seasonality patterns. Conclusion: By emphasizing the impact of mode of arrival in ED context, this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.

🔖

Bu Makaleye Atıf Yap

ATAMAN, M. G., SARIYER, G. (2022). Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments. Eurasian Journal of Emergency Medicine, Cilt: 21 - Sayı: 1, 34–44. https://ajindex.com/articles/507551

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Xu Q, Tsui KL, Jiang W, Guo, H. A hybrid approach for forecasting patient visits in emergency department. Qual Reliab Eng Int. 2016;32:2751-9
  2. Sariyer G, Taşar CÖ, Cepe, GE. Use of data mining techniques to classify length of stay of emergency department patients. Bio-Algorithms and MedSystems. 2019. doi: 10.1515/bams-2018-0044 3. Sariyer G, Ataman MG, Kızıloğlu, İ. Factors affecting length of stay in the emergency department: a research from an operational viewpoint. Int J Healthc Manag. 2018;1-10
  3. Wiler JL, Griffey RT, Olsen, T. Review of modeling approaches for emergency department patient flow and crowding research. Acad Emerg Med. 2011;18:1371-9
  4. Chase VJ, Cohn AE, Peterson TA, Lavieri, MS. Predicting emergency department volume using forecasting methods to create a “surge response” for noncrisis events. Acad Emerg Med. 2012;19:569-76
  5. Hertzum M. Forecasting hourly patient visits in the emergency department to counteract crowding. Ergonomics Open J. 2017;10:1-13
  6. Jones SS, Evans RS, Allen TL, Thomas A, Haug PJ, Welch SJ, et al. multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform. 2009;42:123-139
  7. McCarthy ML, Zeger SL, Ding R, Aronsky D, Hoot NR, Kelen, GD. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15:337-46
  8. Peck JS, Benneyan JC, Nightingale DJ, Gaehde SA. Predicting emergency department inpatient admissions to improve same‐day patient flow. Acad Emerg Med. 2012;19: E1045-54
  9. Sariyer G. Time series modelling for forecasting demand in the emergency department. IJERAD. 2018;10:66-77
  10. Butler MB, Gu H, Kenney T, Campbell SG. Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes. CJEM. 2016;18(S1):S83-4
  11. Bergs J, Heerinckx P, Verelst S. Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. Int Emerg Nurs. 2014;22:112-5
  12. Mai Q, Aboagye‐Sarfo P, Sanfilippo FM, Preen DB, Fatovich DM. Predicting the number of emergency department presentations in W estern A ustralia: A population‐based time series analysis. Emerg Med Australas. 2015;27:16-21
  13. Petrou P. An interrupted time-series analysis to assess impact of introduction of co-payment on emergency room visits in Cyprus. Appl Health Econ Hea. 2015;13:515-23
  14. Rosychuk RJ, Klassen TP, Voaklander DC, Senthilselvan A, Rowe BH. Seasonality patterns in croup prese