Abstract
BackgroundThe Bayesian approach is now widely recognised as a proper framework for analysing risk in health care. However, the traditional text-book Bayesian approach is in many cases difficult to implement, as it is based on abstract concepts and modelling.MethodsThe essential points of the risk analyses conducted according to the predictive Bayesian approach are identification of observable quantities, prediction and uncertainty assessments of these quantities, using all the relevant information. The risk analysis summarizes the knowledge and lack of knowledge concerning critical operations and other activities, and give in this way a basis for making rational decisions.ResultsIt is shown that Bayesian risk analysis can be significantly simplified and made more accessible compared to the traditional text-book Bayesian approach by focusing on predictions of observable quantities and performing uncertainty assessments of these quantities using subjective probabilities.ConclusionThe predictive Bayesian approach provides a framework for ensuring quality of risk analysis. The approach acknowledges that risk cannot be adequately described and evaluated simply by reference to summarising probabilities. Risk is defined by the combination of possible consequences and associated uncertainties.
Highlights
The Bayesian approach is widely recognised as a proper framework for analysing risk in health care
To analyse risk in health care, the Bayesian approach is widely acknowledged as a proper framework, see e.g
Such analyses are seen in health care and patient safety ([7] Marx and Slonim 2003, [11] Battles and Kanki 2004), and in the paper we address their use in this area
Summary
The Bayesian approach is widely recognised as a proper framework for analysing risk in health care. The standard text-book Bayesian analysis ([2] Bernardo and Smith 1994, [3] Singpurwalla 2006) introduces fictional parameters that are difficult to understand and they complicate the analysis. To explain this in more detail, consider a Probabilistic Risk Analysis (PRA) for a specific operation at a specific hospital. To determine p we use models such as event trees and fault trees. Formalising this means that p is computed using a function f of a set of parameters q, i.e. p = f(q). The function f is a model, a representation of the relationship between the "true" parameters p and q
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