RESUMO
Clinical classification systems have proliferated since the APGAR score was introduced in 1953. Numerical scores and classification systems enable qualitative clinical descriptors to be transformed into categorical data, with both clinical utility and ability to provide a common language for learning. The clarity of classification rubrics embedded in a mortality classification system provides the shared basis for discussion and comparison of results. Mortality audits have been long seen as learning tools, but have tended to be siloed within a department and driven by individual learner need. We suggest that the learning needs of the system are also important. Therefore, the ability to learn from small mistakes and problems, rather than just from serious adverse events, remains facilitated.We describe a mortality classification system developed for use in the low-resource context and how it is 'fit for purpose,' able to drive both individual trainee, departmental and system learning. The utility of this classification system is that it addresses the low-resource context, including relevant factors such as limited prehospital emergency care, delayed presentation, and resource constraints. We describe five categories: (1) anticipated death or complication following terminal illness; (2) expected death or complication given clinical situation, despite taking preventive measures; (3) unexpected death or complication, not reasonably preventable; (4) potentially preventable death or complication: quality or systems issues identified and (5) unexpected death or complication resulting from medical intervention. We document how this classification system has driven learning at the individual trainee level, the departmental level, supported cross learning between departments and is being integrated into a comprehensive system-wide learning tool.