Comparison of emergency department time performance between a Canadian and an Australian academic tertiary hospital

Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.

Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single-site validation of a decision tree algorithm

OBJECTIVES

Health services have an imperative to reduce prolonged patient length of stay (LOS) in ED. Our objective is to develop and validate an accurate prediction model for patient LOS in ED greater than 4 hours using a data mining technique.

METHODS

Data were collected from a regional Australian public hospital for all ED presentations between 1 January 2016 and 31 December 2017. A decision tree algorithm was built to predict patients with an ED LOS >4 hours. A total of 33 attributes were analysed. The performance of the final model was internally validated. Clinically relevant patterns from the model were analysed.

RESULTS

The accuracy of the model was 85%. We identified that patients at our site who were at high risk of ED LOS >4 hours were those who were waiting in ED for a medical consultation, or those who were waiting for a urology, surgical, orthopaedic or paediatric consultation if the request for consultation occurred more than 2 hours after the patient was first seen by an ED doctor.

CONCLUSION

This model performed very well in predicting ED LOS >4 hours for each individual patient and demonstrated a number of clinically relevant patterns. Identifying patterns that influence ED LOS is important for health managers in order to develop and implement interventions targeted at those clinical scenarios. Future work should look at the utility of displaying individual patient risk of ED LOS >4 hours using this model in real-time at the point-of-care.