78 research outputs found

    'You can't just hit a button’: an ethnographic study of strategies to repurpose data from advanced clinical information systems for clinical process improvement

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    Background Current policies encourage healthcare institutions to acquire clinical information systems (CIS) so that captured data can be used for secondary purposes, including clinical process improvement. Such policies do not account for the extra work required to repurpose data for uses other than direct clinical care, making their implementation problematic. This paper aims to analyze the strategies employed by clinical units to use data effectively for both direct clinical care and clinical process improvement. Methods Ethnographic methods were employed. A total of 54 contextual interviews with health professionals spanning various disciplines and 18 hours of observation were carried out in 5 intensive care units in England using an advanced CIS. Case studies of how the extra work was achieved in each unit were derived from the data and then compared. Results We found that extra work is required to repurpose CIS data for clinical process improvement. Health professionals must enter data not required for clinical care and manipulation of this data into a machine-readable form is often necessary. Ambiguity over who should be responsible for this extra work hindered CIS data usage for clinical process improvement. We describe 11 strategies employed by units to accommodate this extra work, distributing it across roles. Seven of these motivated data entry by health professionals and four addressed the machine readability of data. Many of the strategies relied heavily on the skill and leadership of local clinical customizers. Conclusions To realize the expected clinical process improvements by the use of CIS data, clinical leaders and policy makers need to recognize and support the redistribution of the extra work that is involved in data repurposing. Adequate time, funding, and appropriate motivation are needed to enable units to acquire and deliver the necessary skills in CIS customization

    The impact of sharing platforms on collaborative design development during emergencies: the case of COVID-19

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    The COVID-19 outbreak resulted in an emergency of projects developed, shared and produced by makers, fablabs and open source enthusiasts. These projects are often released in design sharing platforms, e.g. Thingiverse, Github and Instructables, under open source licenses. It is often argued that the release of such projects holds potential for enhancing collaboration, continuous development and design dissemination. These arguments have been subject of recent studies on the structure of maker/Open Design communities and sharing platforms. This study aims to contribute to the on-going debate on the potentialities of such communities. We adopt an explorative approach to (i) identify the influence of the COVID-19 outbreak on the activity volume of Thingiverse, the object of our study, (ii) analyze the designs metadata and its network patterns, and (iii) identify interaction patterns based on real-world localities. Based on our findings we comment on the importance of the maker/Open Design communities to tackle critical situations and highlight the current limitations for a wider dissemination of open source designs. Our findings may contribute to build better tools for designers and enthusiasts of the maker/open culture as well as to studies on collaborative development

    Artificial Intelligence Based Classification for Urban Surface Water Modelling

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    Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration
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