Forecasting emergency department hourly occupancy using time series analysis.

STUDY OBJECTIVE: To develop a novel predictive model for emergency department (ED) hourly occupancy using readily available data at time of prediction with a time series analysis methodology. METHODS: We performed a retrospective analysis of all ED visits from a large academic center during calendar year 2012 to predict ED hourly occupancy. Due to the time-of-day and day-of-week effects, a seasonal autoregressive integrated moving average with external regressor (SARIMAX) model was selected. For each hour of a day, a SARIMAX model was built to predict ED occupancy up to 4-h ahead. We compared the resulting model forecast accuracy and prediction intervals with previously studied time series forecasting methods. RESULTS: The study population included 65,132 ED visits at a large academic medical center during the year 2012. All adult ED visits during the first 265 days were used as a training dataset, while the remaining ED visits comprised the testing dataset. A SARIMAX model performed best with external regressors of current ED occupancy, average department-wide ESI, and ED boarding total at predicting up to 4-h-ahead ED occupancy (Mean Square Error (MSE) of 16.20, and 64.47 for 1-hr- and 4-h- ahead occupancy, respectively). Our 24-SARIMAX model outperformed other popular time series forecasting techniques, including a 60% improvement in MSE over the commonly used rolling average method, while maintaining similar prediction intervals. CONCLUSION: Accounting for current ED occupancy, average department-wide ESI, and boarding total, a 24-SARIMAX model was able to provide up to 4 h ahead predictions of ED occupancy with improved performance characteristics compared to other forecasting methods, including the rolling average. The prediction intervals generated by this method used data readily available in most EDs and suggest a promising new technique to forecast ED occupancy in real time.

The effects of emergency department crowding on triage and hospital admission decisions.

BACKGROUND: Emergency department (ED) crowding is a recognized issue and it has been suggested that it can affect clinician decision-making. OBJECTIVES: Our objective was to determine whether ED census was associated with changes in triage or disposition decisions made by ED nurses and physicians. METHODS: We performed a retrospective study using one year of data obtained from a US academic center ED (65,065 patient encounters after cleaning). Using a cumulative logit model, we investigated the association between a patient's acuity group (low, medium, and high) and ED census at triage time. We also used multivariate logistic regression to investigate the association between the disposition decision for a patient (admit or discharge) and the ED census at the disposition decision time. In both studies, control variables included census, age, gender, race, place of treatment, chief complaint, and certain interaction terms. RESULTS: We found statistically significant correlation between ED census and triage/disposition decisions. For each additional patient in the ED, the odds of being assigned a high acuity versus medium or low acuity at triage is 1.011 times higher (95% confidence interval [CI] for Odds Ratio [OR] = [1.009,1.012]), and the odds of being assigned medium or high acuity versus low acuity at triage is 1.009 times higher (95% CI for OR = [1.008,1.010]). Similarly, the odds of being admitted versus discharged increases by 1.007 times (95% CI for OR = [1.006,1.008]) per additional patient in the ED at the time of disposition decision. CONCLUSION: Increased ED occupancy was found to be associated with more patients being classified as higher acuity as well as higher hospital admission rates. As an example, for a commonly observed patient category, our model predicts that as the ED occupancy increases from 25 to 75 patients, the probability of a patient being triaged as high acuity increases by about 50% and the probability of a patient being categorized as admit increases by around 25%.

Comparison of emergency department crowding scores: a discrete-event simulation approach.

According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. Using data from a large academic hospital in North Carolina, we evaluate three crowding scores, namely, EDWIN, NEDOCS, and READI by assessing strengths and weaknesses of each score, particularly their predictive power. We perform these evaluations by first building a discrete-event simulation model of the ED, validating the results of the simulation model against observations at the ED under consideration, and utilizing the model results to investigate each of the three ED crowding scores under normal operating conditions and under two simulated outbreak scenarios in the ED. We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.