Understanding accident mechanisms: an analysis of the components of 2516 accidents collected in a MAIM database

Abstract

MAIM is an acronym for the Merseyside Accident Information Model, the current version of which is an intelligent, knowledge-based software system. This paper describes a full scale trial using MAIM to record and categorise data on the causes of injuries. The subjects studied were 2516 patients attending the Royal Liverpool University Hospital for diagnosis and treatment of injuries between September 1992 and September 1993. The aims were to test the MAIM software, to show that it is possible to collect high quality accident information from hospital patients without writing, typing or coding, and to find methods of analysing the database to provide information that can be applied to accident prevention. Subsidiary aims were to show the extent of accidents which occur in sequences of more than one event and to confirm that it is possible to collect routinely the first and final events in accidents. No single description from the accident database could provide a perspective of how the population was injured. The database project has provided evidence on the complexity of accidents and the rare occurrence of identical combinations of all components and there were no two identical accidents. This illustrates the difficulties of preventing accidents. To assist the analysis and to focus attention on information useful for accident prevention, an analysis method has been developed to identify objects and event verbs associated with both the causes of accidents and the causes of injuries. A coefficient can be computed which links events either to the start or end of an accident. The coefficient allows accidents to be grouped so that typical or average accidents can be formulated and accidents with common features can be analysed to show the course of average or typical events reported by patients. This allows a detailed examination of common causes of similar accidents. Difficulties in classifying accidents have been highlighted; this is especially true where accident recording systems attempt to classify accidents into broad groups. The analysis method provides an insight into the mechanisms causing accidents and injuries.