Adaptive regulation or governmentality: patient safety and the changing regulation of medicine

Studies from across the world have shown that clinical mistakes are a major threat to the safety of patient care (World Health Organisation 2004). For the National Health Service (NHS) of England and Wales it is estimated that one in ten hospital patients experience some form of error, and each year these cost the service over £2billion in remedial care (Department of Health 2000). Unsurprisingly, ‘patient safety’ is now a major international health policy priority, questioning the efficacy of existing regulatory practices and proposing a new ethos of learning. Within England and Wales, the National Patient Safety Agency (NPSA) has been created to lead policy development and champion service-wide learning, whilst throughout the NHS the National Reporting and Learning System (NRLS) has been introduced to enable this learning (NPSA 2003). This paper investigates the extent to which, in seeking to better manage the threats to patient safety, this policy agenda represents a transition in medical regulation.

Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.