Visualizing Complex Process Hierarchies during the Modeling Process

Abstract. Clinical practice guidelines are documents that include recommendations describing appropriate care for the management of patients with a specific clinical condition, such as diabetes or chronic heart failure. Several representation languages exist to model these documents in a computer-interpretable and-executable form with the intention of integrating them into clinical information systems. Asbru is one of these representation languages that is able to model the complex hierarchies of these medical processes (called plans in Asbru). To allow their efficient evaluation and manipulation, they must be visualized in a compact and still clear form. This visualization must be integrated into an editing environment which makes changes to the process hierarchy easy and gives immediate feedback on the changes. In this paper, we present a novel visualization, Plan Strips, which represents the hierarchy of plans, i.e., processes, as a set of nested strips. It represents the synchronization of the plans by colour-coding the strips and by the ordering of the strips. This saves considerable space compared to graph representations. The visualization is integrated into an editing environment which allows the immediate modification of the plan hierarchy, but also changes to all other aspects of the plan

Risk-based methods for fish and terrestrial animal disease surveillance

AbstractOver recent years there have been considerable methodological developments in the field of animal disease surveillance. The principles of risk analysis were conceptually applied to surveillance in order to further develop approaches and tools (scenario tree modelling) to design risk-based surveillance (RBS) programmes. In the terrestrial animal context, examples of risk-based surveillance have demonstrated the substantial potential for cost saving, and a similar benefit is expected also for aquatic animals. RBS approaches are currently largely absent for aquatic animal diseases. A major constraint in developing RBS designs in the aquatic context is the lack of published data to assist in the design of RBS: this applies to data on (i) the relative risk of farm sites becoming infected due to the presence or absence of a given risk factor; (ii) the sensitivity of diagnostic tests (specificity is often addressed by follow-up investigation and re-testing and therefore less of a concern); (iii) data on the variability of prevalence of infection for fish within a holding unit, between holding units and at farm level. Another constraint is that some of the most basic data for planning surveillance are missing, e.g. data on farm location and animal movements. In Europe, registration or authorisation of fish farms has only recently become a requirement under EU Directive 2006/88. Additionally, the definition of the epidemiological unit (at site or area level) in the context of aquaculture is a challenge due to the often high level of connectedness (mainly via water) of aquaculture facilities with the aquatic environment. This paper provides a review of the principles, methods and examples of RBS in terrestrial, farmed and wild animals. It discusses the special challenges associated with surveillance for aquatic animal diseases (e.g. accessibility of animals for inspection and sampling, complexity of rearing systems) and provides an overview of current developments relevant for the design of RBS for fish diseases. Suggestions are provided on how the current constraints to applying RBS to fish diseases can be overcome

Exploring health preferences in sociodemographic and health related groups through the paired comparison of the items of the Nottingham Health Profile

BACKGROUND—Preference weighted measures of health related quality of life are necessary for cost effectiveness calculations involving quality of life adjustment. There are conflicting data about the influence of factors such as sociodemographic and health related variables on health preferences.
STUDY OBJECTIVE—The relative values attached to the items of the Spanish version of the Nottingham Health Profile (NHP) were assessed to make comparisons across social and health subgroups.
DESIGN AND PARTICIPANTS—Preference values were obtained in sets of 250 to 253 persons (total n=1258) using the method of paired comparisons after all possible pairs of NHP items had been presented to respondents for judgement of severity. χ(2) Tests and Spearman's correlations among item ranks were calculated.
MAIN RESULTS—Findings show that preferences elicited with the method of paired comparisons are consistent and independent of the sample from which they are obtained (mean correlation coefficients across subgroups range from 0.87 to 0.96). Conclusion—The evaluation of health did not seem to be related to sociodemographic variables (gender, age, social class) or to the health status of the respondents, suggesting that health preferences are stable across different populations.


Keywords: health preferences; Nottingham Health Profile; psychometric