Knowledge-based automatic performance evaluation for medical training debriefing

 Manikin-based medical simulation has been shown to benefit the knowledge, skills and attitudes of the learner, and to impart favourable patient effects. A vital component of any training simulation is the after-session discussion with trainees to debrief their performance. In this study we develop a rule-based debriefing tool for improving the efficacy of medical training sessions. Unlike most existing de-briefing tools, the tool presented here has been designed to reduce medical trainer assessment time and to improve evaluation accuracy through a largely automated evaluation of trainee performance. The developed tool is acknowledged by the School of Medicine of Deakin University as an important advancement in assisting medical trainers carry out the debriefing process effectively and efficiently.

Adoption and maintenance of gym-based strength training in the community setting in adults with excess weight or type 2 diabetes: a randomized controlled trial

BACKGROUND: Participant adoption and maintenance is a major challenge in strength training (ST) programs in the community-setting. In adults who were overweight or with type 2 diabetes (T2DM), the aim of this study was to compare the effectiveness of a standard ST program (SST) to an enhanced program (EST) on the adoption and maintenance of ST and cardio-metabolic risk factors and muscle strength. METHODS: A 12-month cluster-randomized controlled trial consisting of a 6-month adoption phase followed by a 6-month maintenance phase. In 2008-2009, men and women aged 40-75 years (n = 318) with T2DM (n = 117) or a BMI >25 (n = 201) who had not participated in ST previously were randomized into either a SST or an EST program (which included additional motivationally-tailored behavioral counselling). Adoption and maintenance were defined as undertaking ≥ 3 weekly gym-based exercise sessions during the first 6-months and from 6-12 months respectively and were assessed using a modified version of the CHAMPS (Community Healthy Activity Models Program for Seniors) instrument. RESULTS: Relative to the SST group, the adjusted odds ratio (OR) of adopting ST for all participants in the EST group was 3.3 (95 % CI 1.2 to 9.4). In stratified analyses including only those with T2DM, relative to the SST group, the adjusted OR of adopting ST in the EST group was 8.2 (95 % CI 1.5-45.5). No significant between-group differences were observed for maintenance of ST in either pooled or stratified analyses. In those with T2DM, there was a significant reduction in HbA1c in the EST compared to SST group during the adoption phase (net difference, -0.13 % [-0.26 to -0.01]), which persisted after 12-months (-0.17 % [-0.3 to -0.05]). CONCLUSIONS: A behaviorally-focused community-based EST intervention was more effective than a SST program for the adoption of ST in adults with excess weight or T2DM and led to greater improvements in glycemic control in those with T2DM. TRIAL REGISTRATION: Registered at ACTRN12611000695909 (Date registered 7/7/2011).

Investigating the validity and usability of an interactive computer programme for assessing competence in telephone-based mental health triage

Telephone-based mental health triage services are frontline health-care providers that operate 24/7 to facilitate access to psychiatric assessment and intervention for people requiring assistance with a mental health problem. The mental health triage clinical role is complex, and the populations triage serves are typically high risk; yet to date, no evidence-based methods have been available to assess clinician competence to practice telephone-based mental health triage. The present study reports the findings of a study that investigated the validity and usability of the Mental Health Triage Competency Assessment Tool, an evidence-based, interactive computer programme designed to assist clinicians in developing and assessing competence to practice telephone-based mental health triage.

Keyboard control method for virtual reality micro-robotic cell injection training

The rapid development of virtual reality offers significant potential for skills training applications. Our ongoing work proposes virtual reality operator training for the micro-robotic cell injection procedure. The interface between the operator and the system can be achieved in many different ways. The computer keyboard is ubiquitous in its use for everyday computing applications and also commonly utilized in virtual reality systems. Based on the premise that most people have experience in using a computer keyboard, as opposed to more sophisticated input devices, this paper considers the feasibility of using a keyboard to control the micro-robot for cell injection. In this study, thirteen participants underwent the experimental evaluation. The participants were asked to perform three simulated trial sessions in a virtual micro-robotic cell injection environment. Each session consisted of ten cell injection trials and relevant data for each trial were recorded and analyzed. Results showed participants' performance improvement after the three sessions. It was also observed that participants intuitively controlled multiple axes of the micro-robot simultaneously despite the absence of instruction on how to do so. This continued throughout the experiments and suggests skills transfer from other keyboard based interactions. Based on the results provided, it is suggested that keyboard control is a feasible, simple and low-cost control method for the virtual micro-robot.

Building the computational virtual reality environment for anaesthetists' training and practice

Understanding the real world based on visualisation and prediction is essential for the decision-maker. We build a computational virtual reality environment to improve visualisation, understanding and prediction of the physical world and to guide action. It develops a five-dimensional, computer-generated, computational Virtual Reality Environment for Anaesthesia (VREA). Our online prediction will be calculated based on the correlation and composition computing with respect to the three dimensions: horizontal, vertical and individual. The novel musical notes based anesthetic simulator is proposed to identify the abnormality and visualize the online medical time series. The experiments with the online ECG data will present a real-time case to show the effectiveness and efficiency of our proposed system and algorithms.