The information specialist in the clinical team: Building expertise in evidence-based healthcare

The Clinician Development Program (CDP) is an initiative of Queensland Health’s Quality Improvement and Enhancement Program. At the Royal Brisbane & Royal Women's Hospital Health Service Districts, evidence-base practice (EBP) is an important CDP area in which several projects were carried out in 2002. This paper describes one such project. A medical librarian was invited to accompany the clinical team on morning rounds in the Medical Assessment & Planning Unit (MAPU). The librarian conducted information skills training in the ward and helped clinicians to answer questions directly related to patient care. Questions not answered during the round were followed-up, usually within 48 hours, and responses emailed to the consultant who led the rounds. At the project’s conclusion the librarian was invited to continue as a member of the MAPU clinical team, thus acknowledging the valuable role an information specialist can play in incorporating research evidence into patient care. Clinical librarianship (CL) creates a space, albeit a contentious one, for the health librarian at the bedside. This paper describes an Australian CL project and attempts to demystify the role of an information specialist in EBP. It also highlights some of the challenges facing librarians and clinicians attempting to embed EBP in clinical settings.

Using process-oriented holonic (PrOH) modelling to increase understanding of information systems

Methodologies for understanding business processes and their information systems (IS) are often criticized, either for being too imprecise and philosophical (a criticism often levied at softer methodologies) or too hierarchical and mechanistic (levied at harder methodologies). The process-oriented holonic modelling methodology combines aspects of softer and harder approaches to aid modellers in designing business processes and associated IS. The methodology uses holistic thinking and a construct known as the holon to build process descriptions into a set of models known as a holarchy. This paper describes the methodology through an action research case study based in a large design and manufacturing organization. The scientific contribution is a methodology for analysing business processes in environments that are characterized by high complexity, low volume and high variety where there are minimal repeated learning opportunities, such as large IS development projects. The practical deliverables from the project gave IS and business process improvements for the case study company.

About Methods of Mathematical Modelling in the Development of Information Systems

This article describes the approach, which allows to develop information systems without taking into consideration details of physical storage of the relational model and type database management system. Described in terms of graph model, this approach allows to construct several algorithms, for example, for verification application domain. This theory was introduced into operation testing as a part of CASE-system METAS.

Building Data Warehouses Using Numbered Information Spaces

An approach for organizing the information in the data warehouses is presented in the paper. The possibilities of the numbered information spaces for building data warehouses are discussed. An application is outlined in the paper.

Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.

Modeling of Distributed Control Systems in Intelligent Building based on Colored Petri Nets

Distributed control systems consist of sensors, actuators and controllers, interconnected by communication networks and are characterized by a high number of concurrent process. This work presents a proposal for a procedure to model and analyze communication networks for distributed control systems in intelligent building. The approach considered for this purpose is based on the characterization of the control system as a discrete event system and application of coloured Petri net as a formal method for specification, analysis and verification of control solutions. With this approach, we develop the models that compose the communication networks for the control systems of intelligent building, which are considered the relationships between the various buildings systems. This procedure provides a structured development of models, facilitating the process of specifying the control algorithm. An application example is presented in order to illustrate the main features of this approach.

On compliance of business processes with business contracts

This paper addresses the problem of ensuring compliance of business processes, implemented within and across organisational boundaries, with the constraints stated in related business contracts. In order to deal with the complexity of this problem we propose two solutions that allow for a systematic and increasingly automated support for addressing two specific compliance issues. One solution provides a set of guidelines for progressively transforming contract conditions into business processes that are consistent with contract conditions thus avoiding violation of the rules in contract. Another solution compares rules in business contracts and rules in business processes to check for possible inconsistencies. Both approaches rely on a computer interpretable representation of contract conditions that embodies contract semantics. This semantics is described in terms of a logic based formalism allowing for the description of obligations, prohibitions, permissions and violations conditions in contracts. This semantics was based on an analysis of typical building blocks of many commercial, financial and government contracts. The study proved that our contract formalism provides a good foundation for describing key types of conditions in contracts, and has also given several insights into valuable transformation techniques and formalisms needed to establish better alignment between these two, traditionally separate areas of research and endeavour. The study also revealed a number of new areas of research, some of which we intend to address in near future.

Creating proactive interference in immediate recall: Building a DOG from a DART, a MOP, and a FIG

Phonemic codes are accorded a privileged role in most current models of immediate serial recall, although their effects are apparent in short-term proactive interference (PI) effects as well. The present research looks at how assumptions concerning distributed representation and distributed storage involving both semantic and phonemic codes might be operationalized to produce PI in a short-term cued recall task. The four experiments reported here attempted to generate the phonemic characteristics of a nonrhyming, interfering foil from unrelated filler items in the same list. PI was observed when a rhyme of the foil was studied or when the three phonemes of the foil were distributed across three studied filler items. The results suggest that items in short-term memory are stored in terms of feature bundles and that all items are simultaneously available at retrieval.

Modelling dispersal behaviour on a fractal landscape

We use a spatially explicit population model to explore the population consequences of different habitat selection mechanisms on landscapes with fractal variation in habitat quality. We consider dispersal strategies ranging from random walks to perfect habitat selectors for two species of arboreal marsupial, the greater glider (Petauroides volans) and the mountain brushtail possum (Trichosurus caninus). In this model increasing habitat selection means individuals obtain higher quality territories, but experience increased mortality during dispersal. The net effect is that population sizes are smaller when individuals actively select habitat. We find positive relationships between habitat quality and population size can occur when individuals do not use information about the entire landscape when habitat quality is spatially autocorrelated. We also find that individual behaviour can mitigate the negative effects of spatial variation on population average survival and fecundity. (C) 1998 Elsevier Science Ltd. All rights reserved.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

Epidemiological modelling (including economic modelling) and its role in preventive drug therapy

In contrast to curative therapies, preventive therapies are administered to largely healthy individuals over long periods. The risk-benefit and cost-benefit ratios are more likely to be unfavourable, making treatment decisions difficult. Drug trials provide insufficient information for treatment decisions, as they are conducted on highly selected populations over short durations, estimate only relative benefits of treatment and offer little information on risks and costs. Epidemiological modelling is a method of combining evidence from observational epidemiology and clinical trials to assist in clinical and health policy decision-making. It can estimate absolute benefits, risks and costs of long-term preventive strategies, and thus allow their precise targeting to individuals for whom they are safest and most cost-effective. Epidemiological modelling also allows explicit information about risks and benefits of therapy to be presented to patients, facilitating informed decision-making.

Event-related FMRI analysis with maximum correlation modelling

Functional magnetic resonance imaging (FMRI) analysis methods can be quite generally divided into hypothesis-driven and data-driven approaches. The former are utilised in the majority of FMRI studies, where a specific haemodynamic response is modelled utilising knowledge of event timing during the scan, and is tested against the data using a t test or a correlation analysis. These approaches often lack the flexibility to account for variability in haemodynamic response across subjects and brain regions which is of specific interest in high-temporal resolution event-related studies. Current data-driven approaches attempt to identify components of interest in the data, but currently do not utilise any physiological information for the discrimination of these components. Here we present a hypothesis-driven approach that is an extension of Friman's maximum correlation modelling method (Neurolmage 16, 454-464, 2002) specifically focused on discriminating the temporal characteristics of event-related haemodynamic activity. Test analyses, on both simulated and real event-related FMRI data, will be presented.

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.