An economic decision support tool for simulating paratuberculosis control strategies in a UK suckler beef herd

A dynamic, deterministic, economic simulation model was developed to estimate the costs and benefits of controlling Mycobacterium avium subsp. paratuberculosis (Johne's disease) in a suckler beef herd. The model is intended as a demonstration tool for veterinarians to use with farmers. The model design process involved user consultation and participation and the model is freely accessible on a dedicated website. The 'user-friendly' model interface allows the input of key assumptions and farm specific parameters enabling model simulations to be tailored to individual farm circumstances. The model simulates the effect of Johne's disease and various measures for its control in terms of herd prevalence and the shedding states of animals within the herd, the financial costs of the disease and of any control measures and the likely benefits of control of Johne's disease for the beef suckler herd over a 10-year period. The model thus helps to make more transparent the 'hidden costs' of Johne's in a herd and the likely benefits to be gained from controlling the disease. The control strategies considered within the model are 'no control', 'testing and culling of diagnosed animals', 'improving management measures' or a dual strategy of 'testing and culling in association with improving management measures'. An example 'run' of the model shows that the strategy 'improving management measures', which reduces infection routes during the early stages, results in a marked fall in herd prevalence and total costs. Testing and culling does little to reduce prevalence and does not reduce total costs over the 10-year period.

An entropy-based financial decision support system (e-FDSS) for project analysis in construction SMEs

Uncertainty contributes a major part in the accuracy of a decision-making process while its inconsistency is always difficult to be solved by existing decision-making tools. Entropy has been proved to be useful to evaluate the inconsistency of uncertainty among different respondents. The study demonstrates an entropy-based financial decision support system called e-FDSS. This integrated system provides decision support to evaluate attributes (funding options and multiple risks) available in projects. Fuzzy logic theory is included in the system to deal with the qualitative aspect of these options and risks. An adaptive genetic algorithm (AGA) is also employed to solve the decision algorithm in the system in order to provide optimal and consistent rates to these attributes. Seven simplified and parallel projects from a Hong Kong construction small and medium enterprise (SME) were assessed to evaluate the system. The result shows that the system calculates risk adjusted discount rates (RADR) of projects in an objective way. These rates discount project cash flow impartially. Inconsistency of uncertainty is also successfully evaluated by the use of the entropy method. Finally, the system identifies the favourable funding options that are managed by a scheme called SME Loan Guarantee Scheme (SGS). Based on these results, resource allocation could then be optimized and the best time to start a new project could also be identified throughout the overall project life cycle.

Assessing the accuracy of a computerized decision support system for digoxin dosing in primary care: an observational study

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.