Adapting the Analysis to Context
In addition to the developments described above we believe that we may also have to extend our thinking by adapting methods of analysis to the different contexts and models of safety we have outlined. We should be clear at this point that we do not, as yet, know how to do this. Many authors, particularly Erik Hollnagel, have drawn attention to the need for a wider array of accident models which are better adapted to fluid and dynamic environments (Hollnagel 2014). However we do not as yet have sufficient understanding to match models to environments and we have certainly not developed practical methods of analysis which are customized to different contexts.
We can however begin to consider what such an analysis might look like. Suppose we analyse an accident in a very risky unstructured environment – this might be deep sea fishing or an incident that occurred in home care involving someone with serious mental health problems. Are we looking for the same kind of causes and contributory factors as we are in a much more structured environment? The factors might be different and also the balance of factors might be different. For instance the framework of contributory factors (Vincent et al. 1998) identifies patient factors as a potential contributor to an incident. In a highly standardized environment, such as radiotherapy department, personal characteristics play a much less important role than in situations in which a person is responsible for their own care. People with serious mental health or cognitive problems are also clearly at higher risk of making drug errors in their own care. So, the relevance and influence of different types of contributory factors should be different in different contexts. This has, as far as we know, not been addressed empirically but should be entirely feasible. The next step is to ask if we should, in different contexts, be identifying different kinds of recommendations depending on the clinical context. This in turn depends on how one believes safety is achieved and realised in different settings. However before we can fully consider this issue we need to set out our proposals for a strategic approach to safety interventions addressed in the following chapters.
• Every high risk industry devotes considerable time and resource to investigating and analysing accidents, incidents and close calls.
• Effective incident analysis requires a framework which includes guidance on the selection of incidents, and how the investigation and analysis should be conducted.
• Our current framework (known both as ALARM and London Protocol) for incident analysis in medicine: (i) identifies events for analysis chosen by professionals (ii) is based on an underlying safety model examining causes and contributory factors and (iii) uses the 'seven levels of safety' framework to guide the identification of contributory factors and potential interventions.
• The current framework remains relevant, but needs to be significantly adapted to reflect the new safety challenges.
• We need to include events that reflect harm in the eyes of patients who may identify problems that are not necessarily seen by professionals.
• We need to develop an approach which reflects the importance of poor care evolving over time, which in turn affects the nature of the learning and subsequent safety strategies that we implement.
• We propose a new approach to incident analysis (ALARME) which considers contributory factors along the whole patient journey and which includes attention to successes, failures, recovery and mitigation.
• This new approach to incident analysis involves the participation of the patient and family and both hospital and community practitioners. It may require the inclusion of new information such as the patient's personal story of illness and individual laboratory results over time.
• The changes we propose would require significant research and investment in the development of new methods but we believe this is essential if safety is to be effectively managed across clinical contexts.