Use of Emergency Departments by Frail Elderly Patients: Temporal Patterns and Case Complexity.

Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients' case complexity. This study examines differences in arrival rates for frail vs. non-frail patients in detail and compares case complexity in frail patients within vs. outside of regular GP working hours. Arrival times and case variables (admission rate, ED length of stay [LOS], triage level and comorbidities) were extracted from the EHR of an ED in an urban German teaching hospital. We employed Poisson time series regression to determine patterns in hourly arrival rates over the week. Frail elderly patients presented more likely to the ED during already high frequented hours, especially at midday and in the afternoon. Case complexity for frail patients was significantly higher compared to non-frail patients, but varied marginally in time only with respect to triage level and ED LOS. The results suggest that frailty-attuned emergency care should be available in EDs during the busiest hours. Based on EHR data, hospitals thus can tailor their staff needs.

Improving the Prediction of Emergency Department Crowding: A Time Series Analysis Including Road Traffic Flow.

BACKGROUND: Crowding in emergency departments (ED) has a negative impact on quality of care and can be averted by allocating additional resources based on predictive crowding models. However, there is a lack in effective external overall predictors, particularly those representing public activity. OBJECTIVES: This study, therefore, examines public activity measured by regional road traffic flow as an external predictor of ED crowding in an urban hospital. METHODS: Seasonal autoregressive cross-validated models (SARIMA) were compared with respect to their forecasting error on ED crowding data. RESULTS: It could be shown that inclusion of inflowing road traffic into a SARIMA model effectively improved prediction errors. CONCLUSION: The results provide evidence that circadian patterns of medical emergencies are connected to human activity levels in the region and could be captured by public monitoring of traffic flow. In order to corroborate this model, data from further years and additional regions need to be considered. It would also be interesting to study public activity by additional variables.