885 resultados para Rachel vinrace


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A simple and practical technique for assessing the risks, that is, the potential for error, and consequent loss, in software system development, acquired during a requirements engineering phase is described. The technique uses a goal-based requirements analysis as a framework to identify and rate a set of key issues in order to arrive at estimates of the feasibility and adequacy of the requirements. The technique is illustrated and how it has been applied to a real systems development project is shown. How problems in this project could have been identified earlier is shown, thereby avoiding costly additional work and unhappy users.

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This paper describes how the business case can be characterized and used to quickly make an initial and structurally complete goal-responsibility model. This eases the problem of bringing disciplined support to key decision makers in a development project in such a way that it can be instantiated quickly and thereafter support all key decision gateways. This process also greatly improves the understanding shared by the key decision makers and helps to identify and manage load-bearing assumptions.

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Goal modelling is a well known rigorous method for analysing problem rationale and developing requirements. Under the pressures typical of time-constrained projects its benefits are not accessible. This is because of the effort and time needed to create the graph and because reading the results can be difficult owing to the effects of crosscutting concerns. Here we introduce an adaptation of KAOS to meet the needs of rapid turn around and clarity. The main aim is to help the stakeholders gain an insight into the larger issues that might be overlooked if they make a premature start into implementation. The method emphasises the use of obstacles, accepts under-refined goals and has new methods for managing crosscutting concerns and strategic decision making. It is expected to be of value to agile as well as traditional processes.

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Previous work has established the value of goal-oriented approaches to requirements engineering. Achieving clarity and agreement about stakeholders’ goals and assumptions is critical for building successful software systems and managing their subsequent evolution. In general, this decision-making process requires stakeholders to understand the implications of decisions outside the domains of their own expertise. Hence it is important to support goal negotiation and decision making with description languages that are both precise and expressive, yet easy to grasp. This paper presents work in progress to develop a pattern language for describing goal refinement graphs. The language has a simple graphical notation, which is supported by a prototype editor tool, and a symbolic notation based on modal logic.

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This paper describes how the business case can be characterized and used to quickly make an initial and structurally complete goal-responsibility model. This eases the task of bringing disciplined support to key decision makers in a development project in such a way that it can be instantiated quickly and thereafter support all key decisions. This process also greatly improves the understanding shared by the key decision makers and helps to identify and manage loadbearing assumptions. Recent research has revealed two interesting issues, which are highlighted in this paper.

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Background: This study was carried out as part of a European Union funded project (PharmDIS-e+), to develop and evaluate software aimed at assisting physicians with drug dosing. A drug that causes particular problems with drug dosing in primary care is digoxin because of its narrow therapeutic range and low therapeutic index. Objectives: To determine (i) accuracy of the PharmDIS-e+ software for predicting serum digoxin levels in patients who are taking this drug regularly; (ii) whether there are statistically significant differences between predicted digoxin levels and those measured by a laboratory and (iii) whether there are differences between doses prescribed by general practitioners and those suggested by the program. Methods: We needed 45 patients to have 95% Power to reject the null hypothesis that the mean serum digoxin concentration was within 10% of the mean predicted digoxin concentration. Patients were recruited from two general practices and had been taking digoxin for at least 4 months. Exclusion criteria were dementia, low adherence to digoxin and use of other medications known to interact to a clinically important extent with digoxin. Results: Forty-five patients were recruited. There was a correlation of 0·65 between measured and predicted digoxin concentrations (P < 0·001). The mean difference was 0·12 μg/L (SD 0·26; 95% CI 0·04, 0·19, P = 0·005). Forty-seven per cent of the patients were prescribed the same dose as recommended by the software, 44% were prescribed a higher dose and 9% a lower dose than recommended. Conclusion: PharmDIS-e+ software was able to predict serum digoxin levels with acceptable accuracy in most patients.

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This study has explored the underlying causes of preventable drug-related admissions to hospital, from primary care through semi-structured interviews and review of patients’ medical records. Analysis of the data has revealed that communication failures between different groups of healthcare professionals and between healthcare professionals and patients contribute to preventable drug-related admissions, as do knowledge gaps about medication in both healthcare professionals and patients. In addition, working conditions for community pharmacists severely limit their ability to effectively act as a safety barrier to patients receiving inappropriate medication. Limitations include heavy workloads, lack of access to patients’ clinical information, poor relationships with general practitioners and time restrictions. The results of this study represent an important addition to our understanding of the contribution of human error as an underlying cause of preventable drug-related morbidity, and the factors which contribute to errors occurring in the primary healthcare setting.