Managing Cultural Diversity in Healthcare Partnerships: The Case of LIFT

Purpose: The National Health Service (NHS) Local Improvement Finance Trust (LIFT) programme was launched in 2001 as an innovative public-private partnership to address the historical under-investment in local primary care facilities in England. The organisations from the public and private sector that comprise a local LIFT partnership each have their own distinctive norms of behaviour and acceptable working practices - ultimately different organisational cultures. The purpose of this article is to assess the role of organisational culture in facilitating (or impeding) LIFT partnerships and to contribute to an understanding of how cultural diversity in public-private partnerships is managed at the local level. Design/methodology/approach: The approach taken was qualitative case studies, with data gathering comprising interviews and a review of background documentation in three LIFT companies purposefully sampled to represent a range of background factors. Elite interviews were also conducted with senior policy makers responsible for implementing LIFT policy at the national level. Findings: Interpreting the data against a conceptual framework designed to assess approaches to managing strategic alliances, the authors identified a number of key differences in the values, working practices and cultures in public and private organisations that influenced the quality of joint working. On the whole, however, partners in the three LIFT companies appeared to be working well together, with neither side dominating the development of strategy. Differences in culture were being managed and accommodated as partnerships matured. Research limitations/implications: As LIFT develops and becomes the primary source of investment for managing, developing and channelling funding into regenerating the primary care infrastructure, further longitudinal work might examine how ongoing partnerships are working, and how changes in the cultures of public and private partners impact upon wider relationships within local health economies and shape the delivery of patient care. Originality/value: To the authors' knowledge this is the first study of the role of culture in mediating LIFT partnerships and the findings add to the evidence on public-private partnerships in the NHS

ARE ARTIFICIAL NEURAL NETS AS SMART AS A RAT

The origins of artificial neural networks are related to animal conditioning theory: both are forms of connectionist theory, which in turn derives from the empiricist philosophers' principle of association. The parallel between animal learning and neural nets suggests that interaction between them should benefit both sides.

Determining propeller induced erosion alongside quay walls in harbours using Artificial Neural Networks

In shallow waters, such as those found close to berth structures, the wash from a manoeuvring ship’s propeller can cause erosion of the seabed. This erosion can be increased if the wash intersects a berth structure. A number of researchers have undertaken model studies and used regression analysis to develop predictive relationships for the scouring action. This paper presents an experimental investigation with Artificial Neural Networks (ANN’s), used to analyse the results. The purpose of using ANN’s was to examine the prediction accuracy of the Networks in comparison with previous regression analysis methods. ANN’s were found to provide a more accurate method of predicting propeller wash scour than the equations presented by previous investigators.

Demo: Have a Chat with Sensitive Artificial Listeners

Sensitive Artificial Listeners (SAL) are virtual dialogue partners based on audiovisual analysis and synthesis. Despite their very limited verbal understanding, they intend to engage the user in a conversation by paying attention to the user’s emotions and non- verbal expressions. The SAL characters have their own emotionally defined personality, and attempt to drag the user towards their dom- inant emotion through a combination of verbal and non-verbal ex- pression. The demonstrator shows the publicly available, fully au- tonomous SAL system.

Application of artificial neural network techniques to low bit-rate speech coding

Research has been undertaken to investigate the use of artificial neural network (ANN) techniques to improve the performance of a low bit-rate vector transform coder. Considerable improvements in the perceptual quality of the coded speech have been obtained. New ANN-based methods for vector quantiser (VQ) design and for the adaptive updating of VQ codebook are introduced for use in speech coding applications.