A three-dimensional model of error and safety in surgical health care microsystems. Rationale, development and initial testing
Quality, Reliability, Safety and Teamwork Unit, Nuffield Department of Surgical Sciences, University of Oxford, Level 6 John Radcliffe Hospital, Headley Way, Oxford OX3 9DU UK
BMC Surgery 2011, 11:23 doi:10.1186/1471-2482-11-23Published: 5 September 2011
Research estimates of inadvertent harm to patients undergoing modern healthcare demonstrate a serious problem. Much attention has been paid to analysis of the causes of error and harm, but researchers have typically focussed either on human interaction and communication or on systems design, without fully considering the other components. Existing models for analysing harm are principally derived from theory and the analysis of individual incidents, and their practical value is often limited by the assumption that identifying causal factors automatically suggests solutions. We suggest that new models based on observation are required to help analyse healthcare safety problems and evaluate proposed solutions. We propose such a model which is directed at "microsystem" level (Ward and operating theatre), and which frames problems and solutions within three dimensions.
We have developed a new, simple, model of safety in healthcare systems, based on analysis of real problems seen in surgical systems, in which influences on risk at the "microsystem" level are described in terms of only 3 dimensions - technology, system and culture. We used definitions of these terms which are similar or identical to those used elsewhere in the safety literature, and utilised a set of formal empirical and deductive processes to derive the model. The "3D" model assumes that new risks arise in an unpredictable stochastic manner, and that the three defined dimensions are interactive, in an unconstrained fashion. We illustrated testing of the model, using analysis of a small number of incidents in a surgical environment for which we had detailed prospective observational data.
The model appeared to provide useful explanation and categorisation of real events. We made predictions based on the model, which are experimentally verifiable, and propose further work to test and refine it.
We suggest that, if calibrated by application to a large incident dataset, the 3D model could form the basis for a quantitative statistical method for estimating risk at microsystem levels in many acute healthcare settings.