Doctors' thinking about the system as a threat to patient safety

‘Systems thinking’ is an important feature of the emerging ‘patient safety’ agenda. As a key component of a ‘safety culture’, it encourages clinicians to look past individual error to recognise the latent factors that threaten safety. This paper investigates whether current medical thinking is commensurate with the idea of ‘systems thinking’ together with its implications for policy. The findings are based on qualitative semistructured interviews with specialist physicians working within one NHS District General Hospital in the English Midlands. It is shown that, rather then favouring a 'person-centred’ perspective, doctors readily identify ‘the system’ as a threat to patient safety. This is not necessarily a reflection of the prevailing safety discourse or knowledge of policy, but reflects a tacit understanding of how services are (dis)organised. This line of thinking serves to mitigate individual wrong-doing and protect professional credibility by encouraging doctors to accept and accommodate the shortcomings of the system, rather than participate in new forms of organisational learning.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.