MATCH: a new industry-focused approach to medical device development

MATCH (Multidisciplinary Assessment of Technology Centre for Healthcare) is a new collaboration in the UK that aims to support the healthcare sector by creating methods to assess the value of medical devices from concept through to mature product. A major aim of MATCH is to encourage the inclusion of the user throughout the product lifecycle in order to achieve devices that truly meet the requirements of their users. A review of the published literature indicates that user requirements are mainly collected during the design and evaluation stage of the product lifecycle whilst other areas, including the concept stage, have less user involvement. Complementing the literature review is an in-depth consultation with the medical device industry, which has identified a number of barriers encountered by companies when attempting to capture user requirements. These will be addressed by a number of case study projects, performed in collaboration with our industrial partners, that will examine the application and utility of different approaches to collecting and analysing data on user requirements. MATCH is focused on providing advice to device developers on how to select and apply methods that have maximum theoretical strength, practical application, cost-effectiveness and likelihood of wide sector acceptance. Feedback will be sought in order to ensure that the needs of the diverse medical device sector are met.

'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.