David Teubner

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

Streaming occurs in Emergency Department (ED) to reduce crowding, but, misallocation of patients may impact patients' outcome. To determine the outcomes of patients misallocated by the ED process of streaming into likely admission or discharge METHODS: This is a retrospective cohort study, at Australian, urban, tertiary referral hospital's ED between January 2010 to March 2012, using propensity score matching for comparison. Total and partitioned ED lengths of stay, inpatient length of stay, in-hospital mortality, 7- and 28- day unplanned readmission rate were compared between patients who were streamed to be admitted against those streamed to be discharged. Total ED length of stay did not differ significantly for admitted patients if allocated to the wrong stream (median 7.6 hrs, interquartile range 5.7-10.6 cf 7.5 hrs, 5.3-11.2; p = 0.34). The median inpatient length of stay was shorter for those initially misallocated to the discharge stream (1.8 days, 1.1-3.0 cf 2.4 days...

To derive and validate a mortality prediction model from information available at ED triage. Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED. This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition. Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy...