By Rodrigues, S.; Brandão, P.; Nelas, L.; Neves, J.; Alves, V.
Background: In 2000, the Institute of Medicine reported alarming data on the scope and impact of medical errors calling the public attention. One solution to this problem is the adoption of adverse event reporting and learning systems that can help to identify hazards and risks. The accumulation of potentially relevant data in databases contributes little to quality improvement. It is crucial to apply models to identify the adverse events root causes, enhance the sharing of knowledge and experience. The efficiency of the efforts to improve patient safety has been frustratingly slow. Some of this insufficient of progress may be assigned to the lack of systems that take into account the characteristic of the information about the real world. On our daily life, we make most of our decisions, if not all of them, based on incomplete, uncertain and even forbidden or contradictory information. Knowledge is central to the problems of modern economy and society. One’s knowledge is less based on exact facts and more on hypothesis, perceptions or indications. Purpose: From the data collected on our adverse event reporting and learning system, and through Extended Logic Programming and Knowledge Representation, we intend to generate reports that identify the most relevant causes and define improvement strategies, concluding about the impact, place of occurrence, type of form and type of event recorded in the healthcare institutions. Results and Conclusions: The Eindhoven Classification Model was extended and adapted to the medical imaging field and used to classify adverse events root causes. Extended Logic Programming was used for knowledge representation with defective information, allowing for the modelling of the universe of discourse in terms of default data and knowledge. A systematization of the evolution of the body of knowledge about Quality of Information embedded in the Root Cause Analysis was accomplished. An adverse event reporting and learning system was developed based on the presented approach to medical errors in imaging. This system was deployed in two Portuguese healthcare institutions presenting useful results. The system enabled to verify that the majority of occurrences were concentrate in a few events that could be avoided. The developed system allowed automatic knowledge extraction, enabling report generation with strategies for quality improvement.