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 need to be consistent with standard triage scale and the stressful environment contribute to complexity and uncertainty of triage decision-making. This paper proposes a model for mobile decision support that aims at assisting the nurse when determining treatment category of a triage patient. Our model integrates soft computing and mobile computing technologies to provide intelligent decision support. The paper describes the model and prototype implementation of the model.Presented at: IFIP International Conference on Decision Support Systems; 2004 Jul 1-3; Prato, Italy. p. 714-723.Rights: San Pedro, J., Burstein, F., Cao, P., Churilov, L., Zaslavsky, A., and Wassertheil, J. © 2004. The authors grant a non-exclusive licence to publish this document in full in the DSS2004 Conference Proceedings. This document may be published on the World Wide Web, CD-ROM, in printed form, and on mirror sites on the World Wide Web. The authors assign to educational institutions a non-exclusive license to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. Any other usage is prohibited without the express permission of the authors.

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.Methods: A postal survey of 237 paper-based triage scenarios was utilized. A quota sample of triage nurses (n = 42) rated each scenario using the Australasian Triage Scale. The scenarios were analysed for concordance and agreement. The criterion for inclusion of the scenarios in the programme was kappa >= 0.6.Results: Data were collected during 2 April to 14 May 2007. Agreement for the set was kappa = 0.412 (95% CI 0.410-0.415). Of the initial set: 92/237 (38.8%, 95% CI 32.6-45.3) showed concordance >= 70% to the modal triage category (kappa = 0.632, 95% CI 0.629-0.636) and 155/237 (65.4%, 95% CI 59.3-71.5) showed concordance >= 60% to the modal triage category (kappa = 0.507, 95% CI 0.504-0.510). Scenarios involving mental health and pregnancy presentations showed lower levels of agreement (kappa = 0.243, 95% CI 0.237-0.249; kappa = 0.319, 95% CI 0.310-0.328).Conclusion: All scenarios that showed good levels of agreement have been included in the Emergency Triage Education Kit and are recommended for testing purposes; those that showed moderate agreement have been incorporated for teaching purposes. Both scenario sets are accompanied by explanatory notes that link the decision outcome to the Australasian College for Emergency Medicine Guidelines on the Implementation of the Australasian.

This paper describes an evaluation methodology for assessing the decision impact of the intelligent Mobile Decision Support Triage prototype called "iTriage", which was implemented on a handheld personal digital assistant (PDA). The initial evaluation of iTriage by a clinician has provided validation of the proposed model in terms of its capability to for decision support. Since then the recommended changes have been made to iTriage for further testing. The described research evaluates the prototype using lab experiment study design involving nursing students as participants. The study measures the decision impact of iTriage on triage decision making process and outcomes. The paper describes the resulting findings from the data collected and comments on the use and applicability of the mobile DSS prototype for medical triage.

© 2006 The Author Journal compilation © 2006 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine Blackwell Publishing AsiaMelbourne, AustraliaEMMEmergency Medicine Australasia1742-6731© 2006 The Author(s); Journal compilation © 2006 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine200618?317321Editorial EditorialJ Wassertheil

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

The existing models of Emergency Department (ED) operations that are based on the "flow-shop" management logic do not provide adequate decision support in dealing with the ED overcrowding crises. A conceptually different crisis-aware approach to ED modelling and operational decision support is introduced in this paper. It is based on Perrow's theory of "normal accidents" and calls for recognizing the inevitable nature of ED overcrowding crises within current health system setup. Managing the crisis before it happens--a standard approach in crisis management area--should become an integral part of ED operations management. The potential implications of adopting such a crisis-aware perspective for health services research and ED management are outlined.

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.

The complexity of hospital emergency department operations limits comprehension and inhibits efforts to improve efficiency. Attempts have been made to reduce the complexity by streaming patients into similar classes of treatment or grouping them into similar cases. These have not successfully modeled the treatment of patients. This paper describes how the combination of a process philosophy with data mining resulted in the discovery of definitive "treatment pathways". These pathways comprehensively model treatment of patients. Examination of these pathways indicated that the ratio of treatment procedures remained fairly constant. It was concluded that workload in the emergency department varies only by number of presentations, not in type of procedure carried out. Some applications of this knowledge are discussed.

Estimating resource consumption of hospital patients is important for various tasks such as hospital funding, and management and allocation of resources. The common approach is to group patients based on their diagnostic characteristic and infer their resource consumption based on their group membership. This research looks at two alternative forms of grouping of patients based on supervised (classification trees) and unsupervised (self organising map) learning methods. This research is a longitudinal comparison of the effect of supervised and unsupervised learning methods on the groupings of patients. The results for the four-year study indicate that the learning paradigms appear to group patients similarly according to their resource consumption.

Information systems exist for emergency departments (EDIS’), but even the most sophisticated ones concentrate on relatively simple coordination, resource allocation and documentation aspects of emergency department operations. There is little emphasis on management of the treatment process or optimization of resource use because definitive models do not exist for patient treatment processes. This paper outlines the identification of emergency department treatment processes and discusses how this treatment process perspective assists in framing optimization of resource utilisation, clinical decision making, training and emergency department layout.

ABSTRACT Medical perspectives of hospital emergency department operations have been supplemented in recent years by a number of industrial engineering approaches, from simulation to cellular manufacturing. Each approach has furthered understanding of emergency departments’ operations and reliance on other parts of the healthcare system, but have failed to model processes involved in the actual treatment of patients. This paper describes analysis of emergency department processes that provides an alternative to the “outsider” analyses provided by industrial engineering approaches and clinicians’ “insider” analyses. It explains how clusters of patients may be derived from a method commonly employed in data mining. The paper explains how the process-based nature of these patient clusters makes a host of industrial engineering approaches available for use in the analysis of emergency department activities. These industrial engineering approaches have not previously been suitable for application to emergency departments because of the absence of process-based clusters of patients.

This paper describes how key activities in the emergency department of a major hospital were extracted from workflow history. Analysis of these activities help with modification of both administrative and clinical actions for improved efficiency and effectiveness. Extraction of process from data is a relatively new field. This paper’s contributes the innovative determination of processes through data mining, rather than the algorithm-driven approach used to date. Data about patients who present to a major hospital emergency department were used to define clusters of patients who follow common pathways through the emergency department. It is discussed how these “process based” clusters can be used for performance management of the emergency department through evaluation of process inputs, outputs and costs.

Medical Journal of AustraliaVolume 179, Issue 8 p. 451-451 Letter Energy levels for biphasic defibrillation Ian G Jacobs, Corresponding Author Ian G Jacobs Chairman ijacobs@cyllene.uwa.edu.au Australian Resuscitation Council, C/- Royal Australasian College of Surgeons, Spring Street, Melbourne, VIC 3000Correspondence: ijacobs@cyllene.uwa.edu.auSearch for more papers by this authorJames Tibballs, James Tibballs Physician Intensive Care Unit, Royal Children's Hospital, Melbourne, VICSearch for more papers by this authorPeter T Morley, Peter T Morley Nurse Unit Manager Central Gippsland Health Service, Sale, VICSearch for more papers by this authorJennifer Dennett, Jennifer Dennett Director of Emergency Medicine Peninsula Health, Frankston, VICSearch for more papers by this authorJeff Wassertheil, Jeff Wassertheil Head Anaesthesia, Townsville Hospital, Townsville, QLDSearch for more papers by this authorVic Callanan, Vic Callanan Superintendent Divisional Office, Ambulance Service of NSW, Hurstville, NSW.Search for more papers by this authorJohn Hall, John Hall Divisional Office, Ambulance Service of NSW, Hurstville, NSW.Search for more papers by this author Ian G Jacobs, Corresponding Author Ian G Jacobs Chairman ijacobs@cyllene.uwa.edu.au Australian Resuscitation Council, C/- Royal Australasian College of Surgeons, Spring Street, Melbourne, VIC 3000Correspondence: ijacobs@cyllene.uwa.edu.auSearch for more papers by this authorJames Tibballs, James Tibballs Physician Intensive Care Unit, Royal Children's Hospital, Melbourne, VICSearch for more papers by this authorPeter T Morley, Peter T Morley Nurse Unit Manager Central Gippsland Health Service, Sale, VICSearch for more papers by this authorJennifer Dennett, Jennifer Dennett Director of Emergency Medicine Peninsula Health, Frankston, VICSearch for more papers by this authorJeff Wassertheil, Jeff Wassertheil Head Anaesthesia, Townsville Hospital, Townsville, QLDSearch for more papers by this authorVic Callanan, Vic Callanan Superintendent Divisional Office, Ambulance Service of NSW, Hurstville, NSW.Search for more papers by this authorJohn Hall, John Hall Divisional Office, Ambulance Service of NSW, Hurstville, NSW.Search for more papers by this author First published: 20 October 2003 https://doi.org/10.5694/j.1326-5377.2003.tb05631.xCitations: 5Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume179, Issue8October 2003Pages 451-451 RelatedInformation

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.

Emergency MedicineVolume 14, Issue 2 p. 136-138 Master of Clinical Education (University of New South Wales) Jeff Wassertheil, Corresponding Author Jeff Wassertheil Departments of Emergency Medicine, Peninsula Health, Medicine, Southern Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria and Professor Jeff Wassertheil, Department of Emergency Medicine, Peninsula Health, Melbourne, Vic. 3199, Australia; Email: Jwassertheil@phcn.vic.gov.auSearch for more papers by this authorShane Curran, Shane Curran Emergency Department, Wagga Wagga Base Hospital, School of Rural Health, University of New South Wales, NSW, AustraliaSearch for more papers by this author Jeff Wassertheil, Corresponding Author Jeff Wassertheil Departments of Emergency Medicine, Peninsula Health, Medicine, Southern Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria and Professor Jeff Wassertheil, Department of Emergency Medicine, Peninsula Health, Melbourne, Vic. 3199, Australia; Email: Jwassertheil@phcn.vic.gov.auSearch for more papers by this authorShane Curran, Shane Curran Emergency Department, Wagga Wagga Base Hospital, School of Rural Health, University of New South Wales, NSW, AustraliaSearch for more papers by this author First published: 04 July 2002 https://doi.org/10.1046/j.1442-2026.2002.00331.xCitations: 1 Jeff Wassertheil, Associate Professor/Director of Emergency Medicine, Peninsula Health and Department of Medicine, Monash University; Shane Curran MB BS, FACEM, Emergency Physician, Wagga Wagga Base Hospital, Lecturer in Emergency Medicine, School of Rural Health, University of New South Wales. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume14, Issue2June 2002Pages 136-138 RelatedInformation