School of Health and Life Sciences – Virtual Worlds

Accurate selection of exposure factors when x-raying patients is of vital importance to ensure repeat exposures are not required. Due to the use of ionising radiation it is not possible for students to currently practice giving people a range of exposures to see what effect manipulation of exposure factors would have. Experience is currently gained by working alongside clinicians in the clinical environment. Feedback from students currently suggests that selection of exposure factors is an area where they feel they have insufficient practical experience. Giving students a tool where they can practice cause and effect on images dependant on exposure factor selection would enable students to develop necessary practical skills in a safe way.

A virtual x-ray environment is currently under development on Second Life, the aim of which is to enable undergraduate diagnostic imaging students to manipulate exposure factors for a chosen patient and body part. The resultant image will be displayed thereby enabling the student to gain skills in choosing accurate exposure factors. The student will be able to visualise whether their selected exposure factors, variables being kV, mA, time and distance, would have resulted in the image being under-,correctly or over-exposed. It is hoped that over time students will be able to select from a range of options e.g. gender, age, body build, body part.

For more info leave a comment for Jan little aka Bod Mavendorf

A little sampler video sequence to show our work on connecting “Second Life” avatar-patient-bots with specially written AIML [artificial intelligence markup language] and speech synthesis software.
Hopefully we’ll add speech recognition too This project is being used with Nurse Practitioner students. However it could be used for a number of healthcare professionals. Please feel free to add a comment.

There’s more info below, see the post on   Sep 16 2009 “Taking a patient history from a virtual patient”

This was a small scale qualitative evaluation of the lecturers’ (researchers’) and students’ experience of using Second Life in order to learn decision-making. Students assumed Avatars (a student nurse image in Second Life) and accessed the Clinical Simulation Laboratory within Second Life following changing into nursing uniform. They then worked through a scenario based activity. Over a six week period each student carried out a communication and visual assessment of six patients in one ward based area (one session for each student). At the end of the session the student was asked to prioritise the care for these patients and explain their decisions. The students then recorded their experiences during a one-to-one semi-structured interview and focus group.

The research team acted as the students’ mentors via their individual Avatars and provided feedback and guidance to the student Avatar.
A pragmatic approach was taken that included elements of:
• Case based learning: six realistic, interactive scenarios were developed at the appropriate level for the students
• Discovery learning: students interacted with others within the clinical simulation environment providing opportunities for incidental learning
• Reflective Learning: students used interview to record activities and reflect on their relevance to nursing theory and practice

This is Colin. He is a virtual patient Bot who can talk. He is used to help qualified nurses to take a diagnostic patient history. He also has embedded heart sounds in his chest. The students can click on his chest in the correct anatomical position to hear the heart sounds which link back to the history they have just taken. Keep your eyes peeled for a video demo of Colin Soon!

Regards Kali Pizzaro aka real Life Evelyn McElhinney Lecturer, Andy Whiteford Technician aka AndyW Blackburn

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Someof our patient bots

Welcome to Glasgow Caledonian SOH – Virtual Worlds blog. This blog will be used to share the experiences and projects the School of Health are undertaking in Second Life and other virtual worlds.

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