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.
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: Rafael Calegari; Flavio S Fogliatto; Filipe R Lucini; Jeruza Neyeloff; Ricardo S Kuchenbecker; Beatriz D Schaan Journal: Comput Math Methods Med Date: 2016-09-20 Impact factor: 2.238
Authors: Michael M Dinh; Saartje Berendsen Russell; Kendall J Bein; Kris Rogers; David Muscatello; Richard Paoloni; Jon Hayman; Dane R Chalkley; Rebecca Ivers Journal: BMC Emerg Med Date: 2016-12-03
Authors: Christopher M Pearce; Adam McLeod; Jon Patrick; Douglas Boyle; Marianne Shearer; Paula Eustace; Mary Catherine Pearce Journal: JMIR Res Protoc Date: 2016-12-20