Objectives: To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data.Methods: Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model. Results:Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well. Conclusions:A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

BackgroundAccurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.MethodsData for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.ResultsBy time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.ConclusionTime series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

Older patients admitted to an EDOU are an at-risk group and benefit from geriatric assessment before discharge.

Frontline healthcare workers (HCWs) fighting COVID-19 have been associated with depression and anxiety, but there is limited data to illustrate these changes over time. We aim to quantify the changes in depression and anxiety amongst Emergency Department (ED) HCWs over one year and examine the factors associated with these changes. In this longitudinal single-centre study in Singapore, all ED HCWs were prospectively recruited face-to-face. Paper-based surveys were administered in June 2020 and June 2021. Depression and anxiety were measured using DASS-21. The results of 241 HCWs who had completed both surveys were matched. There was significant improvement in anxiety amongst all HCWs (Mean: 2020: 2.85 (±3.19) vs. 2021: 2.54 (±3.11); Median: 2020: 2 (0–4) vs. 2021: 2 (0–4), p = 0.045). HCWs living with elderly and with concerns about infection risk had higher odds of anxiety; those living with young children had lower odds of anxiety. There was significant worsening depression amongst doctors (Mean: 2020: 2.71 (±4.18) vs. 2021: 3.60 (±4.50); Median: 2020: 1 (0–3) vs. 2021: 3 (0–5), p = 0.018). HCWs ≥ 41 years, living with elderly and with greater concerns about workload had higher odds of depression. HCWs who perceived better workplace support and better social connectedness had lower odds of depression. In summary, our study showed significant improvement in anxiety amongst ED HCWs and significant worsening depression amongst ED doctors over one year. Age, living with elderly, and concerns about workload and infection risk were associated with higher odds of depression and anxiety.