Trial of the Primary Care Practice Improvement Tool: Building organisational performance in Australian general practice and primary health care

Objective: To nationally trial the Primary Care Practice Improvement Tool (PC-PIT), an organisational performance improvement tool previously co-created with Australian primary care practices to increase their focus on relevant quality improvement (QI) activities. Design: The study was conducted from March to December 2015 with volunteer general practices from a range of Australian primary care settings. We used a mixed-methods approach in two parts. Part 1 involved staff in Australian primary care practices assessing how they perceived their practice met (or did not meet) each of the 13 PC-PIT elements of high-performing practices, using a 1–5 Likert scale. In Part 2, two external raters conducted an independent practice visit to independently and objectively assess the subjective practice assessment from Part 1 against objective indicators for the 13 elements, using the same 1–5 Likert scale. Concordance between the raters was determined by comparing their ratings. In-depth interviews conducted during the independent practice visits explored practice managers’ experiences and perceived support and resource needs to undertake organisational improvement in practice. Results: Data were available for 34 general practices participating in Part 1. For Part 2, independent practice visits and the inter-rater comparison were conducted for a purposeful sample of 19 of the 34 practices. Overall concordance between the two raters for each of the assessed elements was excellent. Three practice types across a continuum of higher- to lower-scoring practices were identified, with each using the PC-PIT in a unique way. During the in-depth interviews, practice managers identified benefits of having additional QI tools that relate to the PC-PIT elements. Conclusions: The PC-PIT is an organisational performance tool that is acceptable, valid and relevant to our range of partners and the end users (general practices). Work is continuing with our partners and end users to embed the PC-PIT in existing organisational improvement programs.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.