Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province

OBJECTIVE

To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model.

METHODS

The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared.

RESULTS

The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect.

CONCLUSION

Both models can better fit the number of admissions, and SARIMA model has better prediction effect.

Dynamics in the Neurotrauma Catchment Area of a German University Hospital during the COVID-19 Pandemic

Objective: At the beginning of 2020, the COVID-19 pandemic enforced a rapid reallocation of healthcare resources. Our neurosurgical department is located in the German county of Marburg−Biedenkopf, about 80 km from the nearest major city. We were able to maintain our previously established open-door policy after the emergence of COVID-19. Here, we report on dynamics in the catchment area for neurotrauma patients at our department during the pandemic. Methods: 763 consecutive neurotrauma cases admitted to our department between 1 January 2018 and 31 December 2021 were analyzed retrospectively. Patients’ age, gender, origin, diagnoses, and outcomes were recorded. The number of patients hospitalized with a COVID-19 infection in Germany (PHCG) were retrieved from the Robert Koch Institute (RKI). We defined calendar weeks with >1000 PHCG as high COVID-19 caseload weeks (HCLW). Chi-square and Fisher’s exact served as statistical tests. Results: In 2020 and 2021, we observed a significantly increased number of neurotrauma patients who, with primary residence outside of our district, were admitted to our hospital compared to 2018 and 2019 (p < 0.001), while there were no significant differences in in-house mortality. During HCLW, a significantly increased number of neurotrauma patients with primary residence in the densely populated southwestern margin (SWM) of the contiguous part of our catchment area were referred to us compared to the time prior to the pandemic and between HCLW (p = 0.003). In neurotrauma patients admitted from the SWM during HCLW, there was no tendency towards higher in-house mortality. Conclusion: An open-door policy may moderate the risk of involuntarily triaging neurotrauma patients during a pandemic.

Effects of weather, air pollution and Oktoberfest on ambulance-transported emergency department admissions in Munich, Germany

BACKGROUND

Climate change and increasing risks of extreme weather events affect human health and lead to changes in the emergency department (ED) admissions and the emergency medical services (EMS) operations. For a better allocation of resources in the healthcare system, it is essential to predict ED numbers based on environmental variables. This publication aims to quantify weather, air pollution and calendar-related effects on daily ED admissions.

METHODS

Analyses were based on 575,725 admissions from the web-based IVENA system recording all patients in the greater Munich area with pre-hospital emergency care in ambulance operations during 2014-2018. Linear models were used to identify statistically significant associations between daily ED admissions and calendar, meteorological and pollution factors, allowing for lag effects of one to three days. Separate analyses were performed for seasons, with additional subset analyses by sex, age and surgical versus internal department.

RESULTS

ED admissions were exceptionally high during the three-week Oktoberfest, particularly for males and on the weekends, as well as during the New Year holiday. Admissions significantly increased during the years of study, decreased in spring and summer holidays, and were lower on Sundays while higher on Mondays. In the warmer seasons, admissions were significantly associated with higher temperature, adjusting for the effects of sunshine and humidity in all age groups except for the elderly. Adverse weather conditions in non-summer seasons were either linked to increasing ED admissions (from storms, gust) or decreasing them from rain. Mostly, but not exclusively, in winter, increasing ED admissions were associated with colder minimum temperatures as well as with higher NO and PM₁₀ concentrations.

CONCLUSIONS

In addition to standard calendar-related factors, incorporating seasonal weather, air pollutant and interactions with patient demographics into resource planning models can improve the daily allocation of resources and staff of EMS operations at hospital and city levels.

Centralized Ambulance Diversion Policy Using Rolling-Horizon Optimization Framework to Minimize Patient Tardiness

Ambulance diversion (AD) is a common method for reducing crowdedness of emergency departments by diverting ambulance-transported patients to a neighboring hospital. In a multi-hospital system, the AD of one hospital increases the neighboring hospital's congestion. This should be carefully considered for minimizing patients' tardiness in the entire multi-hospital system. Therefore, this paper proposes a centralized AD policy based on a rolling-horizon optimization framework. It is an iterative methodology for coping with uncertainty, which first solves the centralized optimization model formulated as a mixed-integer linear programming model at each discretized time, and then moves forward for the time interval reflecting the realized uncertainty. Furthermore, the decentralized optimization, decentralized priority, and No-AD models are presented for practical application, which can also show the impact of using the following three factors: centralization, mathematical model, and AD strategy. The numerical experiments conducted based on the historical data of Seoul, South Korea, for 2017, show that the centralized AD policy outperforms the other three policies by 30%, 37%, and 44%, respectively, and that all three factors contribute to reducing patients' tardiness. The proposed policy yields an efficient centralized AD management strategy, which can improve the local healthcare system with active coordination between hospitals.