Jeff Wassertheil

Optimizing triage consistency in Australian emergency departments: the Emergency Triage Education Kit.

Objective:  The Emergency Triage Education Kit was designed to optimize consistency of triage using the Australasian Triage Scale. The present study was conducted to determine the interrater reliability of a set of scenarios for inclusion in the programme.

On Development and Evaluation of Prototype Mobile Decision Support for Hospital Triage

Ambiguous triage scenarios in hospital emergency departments are often difficult to assess without decision support. Subjective assessments of such scenarios can either lead to under-triaging or over-triaging for which true conditions of patients are often not addressed within the required time. In this paper, we propose a decision support model that can guide a clinician when identifying the urgency of medical intervention when patient presents with ambiguous triage case. Our model is a heuristic approach that selects the best triage category, identifies corresponding discriminating attribute of the patient, and allows clinician to attach a level of confidence in the decision. We implemented this model as a mobile decision support system, called iTriage. Results of an initial evaluation of iTriage using fourteen paper-based adult triage scenarios showed that our model produced robust decisions for urgent scenarios. For non-urgent scenarios, the proposed model provided guidance especially when the scenarios were ambiguously stated.

Mobile decision support for triage in emergency departments

Triage in emergency departments is often complex and subject to conditions of uncertainty. The need for timely and accurate clinical assessment based on restricted and ambiguous information; the ne ...

Australian Resuscitation Guidelines: applying the evidence and simplifying the process.

© 2006 The Author Journal compilation

A Comparison of Patient Classification Using Data Mining in Acute Health Care

Patients’ diagnoses are used currently as a basis for resource consumption. There are other alternative forms of groupings: one approach is to group patients according to common characteristics and infer their resource consumption based on their group membership. In this paper, we compare the effectiveness of the alternative forms of patient classification obtained from data mining with the current classification for an objective assessment of the average difference between the inferred and the actual resource consumption. In tackling this prediction tasks, classification trees and neural clustering are used. Demographic and hospital admission information is used to generate the clusters and decision tree nodes. For the case study under consideration, the alternative forms of patient classifications seem to be better able to reflect the resource consumption than diagnosis related groups.

Intelligent Multiattribute Decision Support Model for Medical Triage

The paper describes a model for intelligent multiattribute decision support for medical triage. Triage is a preliminary clinical assessment of a patient aimed at categorising the treatment category according to priority level or urgency. The model uses a combination of rule-based reasoning and multiattribute decisionmaking to assist a nurse in selecting the best treatment category for a patient. The model uses a combination of rule-based reasoning and multiattribute decisionmaking to assist a nurse in selecting the best treatment category for a patient in emergency care. The proposed model can potentially address the issues of accuracy, consistency and timeliness in medical triage decisions.