BACKGROUND: A variety of systems have been applied to identify and address errors in the management of multiple @entity130 @entity1 . This lack of standardisation represents a serious problem. OBJECTIVES: Detect preventable and potentially preventable @entity204 , and classify all the errors with universal language. METHODS: We studied all @entity130 @entity1 over 16 admitted to the critical care unit or who died before. In multidisciplinary sessions we decided which @entity204 were preventable, potentially preventable and non preventable. Guided by ATLS protocols, we detected errors in their management that were classified using the taxonomy of Joint Commission. RESULTS: We registered 1236 @entity130 @entity1 (ISS 20.77). Of the 115 @entity130 @entity204 , 19 were preventable or potentially preventable @entity204 . We recorded 130 errors in all @entity204 , 46 of them in preventable or potentially preventable @entity204 . Using our own classification, the main errors were delay in starting correct treatment or performance of CT in hemodynamically unstable @entity1 . Using the taxonomy of Joint Commission, the main type error was clinical, during the intervention: the delay in initiating correct treatment. Mistakes were made in the emergency department by medical specialists. The incidence of therapeutic and diagnostic errors was similar. The main cause of error was @entity1 failure, specifically 'rule-based' errors CONCLUSIONS: Measuring and recording the results is the first step on the way to improving the quality of care for @entity130 @entity1 . A common language like the taxonomy of Joint Commission will help standardise @entity1 safety data, thus improving the recording of incidents and their analysis and treatment.

Preventable XXXX and potentially preventable @entity204 . What are our errors?

@entity1 :: ('9606', 'Species') :: ['human', 'patients', 'patient'] @entity204 :: ('MESH:D003643', 'Disease') :: ['deaths'] @entity130 :: ('MESH:D014947', 'Disease') :: ['trauma']
@entity204 :: (MESH:D003643,Disease) :: ['deaths']
