Benefits realisation for healthcare

Following the emergent importance of benefits realisation applied to healthcare infrastructure and service development programs, HaCIRIC has undertaken a research initiative targeting the development of a robust and comprehensive Benefits Realisation (BeReal©) process. The resulting model is focusing on how benefits should be elicited at the initial strategic stages, and how benefits should be deployed, managed and traced along the lifecycle of a programme so their realisation contributes to successful health outcomes. Subsequently BeReal© aspires to be an appropriate method to drive and control the programme plan; providing tools and techniques for defining specific benefits. It also allows the measurement and evaluation of the extent to which those benefits are delivered. We have set ourselves the objective of identifying current best practices and demonstrate how to improve benefits realisation in healthcare infrastructure provision. The HaCIRIC team in active collaboration with leading industry partners have undertaken various case and comparator studies not only to define a business critical process but to set out an ideology which places benefits realisation at the heart of securing wholly integrated (collective) change. We believe that to deliver consistent high quality infrastructure and services within an ever changing investment model requires a different level of thinking and understanding towards benefits realisation. The challenge of answering community needs through intelligent investment in infrastructure is complex and demands a deeper and inclusive awareness and appreciation of how to deliver benefits and effectively allocate resources. The BeReal© initiative seeks to contribute methodologically and intends to help spending money intelligently, working with programme and project related stakeholders, securing that the best possible benefits are obtained for the overall healthcare communities. This report highlights selected performed initiatives and summarises BeReal© process’s major characteristics, covering far more than the follow-up of a competitive tendering process and of the development of a traditional business case. BeReal© copes with a detailed definition of changing activities, breakdown of (needs into) benefits that drive the investment, supports decision-making, proposes the development of controlling initiatives and suggests major awareness to the implementation of corrective actions. We seek to continue innovating, stimulate learning, contributing to an increase of health and care performance that properly answers to community needs and intelligently invests public and private resources

Inferring adaptive codon preference to understand sources of selection shaping codon usage bias.

Alternative synonymous codons are often used at unequal frequencies. Classically, studies of such codon usage bias (CUB) attempted to separate the impact of neutral from selective forces by assuming that deviations from a predicted neutral equilibrium capture selection. However, GC-biased gene conversion (gBGC) can also cause deviation from a neutral null. Alternatively, selection has been inferred from CUB in highly expressed genes, but the accuracy of this approach has not been extensively tested, and gBGC can interfere with such extrapolations (e.g., if expression and gene conversion rates covary). It is therefore critical to examine deviations from a mutational null in a species with no gBGC. To achieve this goal, we implement such an analysis in the highly AT rich genome of Dictyostelium discoideum, where we find no evidence of gBGC. We infer neutral CUB under mutational equilibrium to quantify “adaptive codon preference,” a nontautologous genome wide quantitative measure of the relative selection strength driving CUB. We observe signatures of purifying selection consistent with selection favoring adaptive codon preference. Preferred codons are not GC rich, underscoring the independence from gBGC. Expression-associated “preference” largely matches adaptive codon preference but does not wholly capture the influence of selection shaping patterns across all genes, suggesting selective constraints associated specifically with high expression. We observe patterns consistent with effects on mRNA translation and stability shaping adaptive codon preference. Thus, our approach to quantifying adaptive codon preference provides a framework for inferring the sources of selection that shape CUB across different contexts within the genome

Gastrointestinal helminths in calves and cows in an organic milk production system

The main aim of this study was to determine the distribution of populations of gastrointestinal helminths in lactating crossbred cows and calves during the grazing season in an organic milk production system. In addition, the potential importance of the peripartum in relation to the parasite load was examined. Between January 2007 and December 2008, parasitological fecal examinations were performed on cattle belonging to the Integrated Animal Production Program of Embrapa Agrobiology. The cows' parasite load remained low during the study period, and there were no statistical differences (p > 0.05) in comparisons between the seasons. The average egg count showed a positive correlation (0.80) with the peripartum, such that egg elimination per gram (p < 0.05) was higher during the week of labor than during the pre and postpartum periods. Calves showed low parasite loads, with significantly higher egg elimination (p < 0.05) during the winter. The study indicated that infection with gastrointestinal helminths was not a limiting factor for milk production in the organic system. Specifically, it was concluded that the nematode load can be maintained at moderate levels throughout the production system, even in the absence of anthelmintic treatment

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.&#xd;&#xa;In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior