A university clinic: an innovative model for improving clinical practice

Objective: This paper discusses interprofessional learning through interdisciplinary collaboration between undergraduate nursing and podiatry students at a university based cardiovascular screening clinic. Setting: A cardiovascular risk assessment clinic at a university campus in rural New South Wales, Australia. Subjects: Students from nursing and podiatry enrolled in a baccalaureate degree at Charles Sturt University (CSU) Albury Wodonga Campus. Main Outcome Measures: Undergraduate health care students develop their clinical skills primarily through clinical rotation within accredited health care facilities. The value of these clinical placements to students is dependent upon availability, the quality of the facilitation and the perceived burden to the organisation of providing places for students. There is increasing competition for these clinical places with health care service managers and their staff frequently highly stressed due to increased demand for clinical services, workforce shortages and increasing fiscal constraints. As a consequence of these challenges, an innovative, interdisciplinary program, designed to both improve and extend the repertoire of clinical skills of undergraduate health care students was piloted at a regional university campus. The pilot program used an established community screening clinic conducted by CSU. Students were invited to attend the clinic and undertake assessment tasks relevant to their profession and curriculum requirements as well as learn and practice clinical skills outside their usual professional practice. Students were able to practise these skills in a supportive environment, without the inherent time constraints and pressures experienced in health care facilities. The effectiveness of this model was evaluated through interviews as well as a pre and post test evaluation of one clinical skill. Students reported enthusiasm towards the program and were particularly interested in the opportunity of working in an interprofessional community focussed context and having the opportunity to expand their scope of practice

Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes