Tags

Type your tag names separated by a space and hit enter

Predicting emergency department admissions.
Emerg Med J. 2012 May; 29(5):358-65.EM

Abstract

OBJECTIVE

To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year.

METHODS

Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data.

RESULTS

Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE ∼7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers.

CONCLUSIONS

Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies.

Authors+Show Affiliations

CSIRO Information and Communication Technologies Centre, Level 5, UQ Health Sciences Building, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia. justin.boyle@csiro.auNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

21705374

Citation

Boyle, Justin, et al. "Predicting Emergency Department Admissions." Emergency Medicine Journal : EMJ, vol. 29, no. 5, 2012, pp. 358-65.
Boyle J, Jessup M, Crilly J, et al. Predicting emergency department admissions. Emerg Med J. 2012;29(5):358-65.
Boyle, J., Jessup, M., Crilly, J., Green, D., Lind, J., Wallis, M., Miller, P., & Fitzgerald, G. (2012). Predicting emergency department admissions. Emergency Medicine Journal : EMJ, 29(5), 358-65. https://doi.org/10.1136/emj.2010.103531
Boyle J, et al. Predicting Emergency Department Admissions. Emerg Med J. 2012;29(5):358-65. PubMed PMID: 21705374.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Predicting emergency department admissions. AU - Boyle,Justin, AU - Jessup,Melanie, AU - Crilly,Julia, AU - Green,David, AU - Lind,James, AU - Wallis,Marianne, AU - Miller,Peter, AU - Fitzgerald,Gerard, Y1 - 2011/06/24/ PY - 2011/6/28/entrez PY - 2011/6/28/pubmed PY - 2012/7/18/medline SP - 358 EP - 65 JF - Emergency medicine journal : EMJ JO - Emerg Med J VL - 29 IS - 5 N2 - OBJECTIVE: To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. METHODS: Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data. RESULTS: Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE ∼7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers. CONCLUSIONS: Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies. SN - 1472-0213 UR - https://www.unboundmedicine.com/medline/citation/21705374/Predicting_emergency_department_admissions_ L2 - http://emj.bmj.com/cgi/pmidlookup?view=long&pmid=21705374 DB - PRIME DP - Unbound Medicine ER -