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 data and knowledge default. 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, with an appealing outcome. The system enabled to verify that the majority of occurrences were concentrated in a few events that could be avoided. The developed system allowed automatic knowledge extraction, enabling report generation with strategies for the improvement of quality-of-care.

In 2000, the Institute of Medicine's report, To Err Is Human: Building a Safer Health System, caught the public attention documenting the magnitude of the medical error problem and the inherent patient safety: medical errors cause between 44,000 and 98,000 deaths annually in the United States. Currently, there is a growing interest in risk management on the medical field, particularly in the management of adverse events. It has been mainly due to the commitment of the World Health Organization, that this field of research has gained increasing the attention it deserves. Medical imaging is one of the high risk fields for the occurrence of errors, especially due to the multiplicity of techniques, the several stakeholders and the complexity of the whole circuit that involves the conduct of studies. Many of the methods used to analyze patient safety were adapted from risk-management techniques in high-risk industries (e.g. chemical, nuclear power and aviation industry). It is recognized that we can learn more from our mistakes than from our successes and the reporting systems in these industries have provided a valuable contribution to error prevention and risk management techniques. At a minimum, adverse events reporting systems can help to identify hazards and risks, providing important information on the system aspects that should be improved. However, the accumulation of potentially relevant data contributes little to healthcare services improvement. It is crucial to apply models to identify the underlying system failures, the root causes, and enhance the sharing of knowledge and experience. In this paper, it is suggested a solution to reduce adverse events, by identifying and eliminating the root causes that are in their source. How the Eindhoven Classification Model was adapted and extended specifically for the Medical Imaging field is also presented. The proposed approach includes the root causes analysis and introduces incomplete information concepts through the use of logical-mathematical operators formally sustained. This model is the basis of the adverse event and near misses reporting and learning system that was developed for Medical Imaging and is implemented in two Portuguese healthcare institutions. The objectives, characteristics and function of this system are presented throughout this article.

Web-based teaching via Intelligent Tutoring Systems (ITSs) is considered as one of the most successful enterprises in artificial intelligence. Indeed, there is a long list of ITSs that have been tested on humans and have proven to facilitate learning, among which we may find the well-tested and known tutors of algebra, geometry, and computer languages. These ITSs use a variety of computational paradigms, as production systems, Bayesian networks, schematemplates, theorem proving, and explanatory reasoning. The next generation of ITSs are expected to go one step further by adopting not only more intelligent interfaces but will focus on integration. This article will describe some particularities of a tutoring system that we are developing to simulate conversational dialogue in the area of Medicine, that enables the integration of highly heterogeneous sources of information into a coherent knowledge base, either from the tutor's point of view or the development of the discipline in itself, i.e. the system's content is created automatically by the physicians as their daily work goes on. This will encourage students to articulate lengthier answers that exhibit deep reasoning, rather than to deliver straight tips of shallow knowledge. The goal is to take advantage of the normal functioning of the health care units to build on the fly a knowledge base of cases and data for teaching and research purposes.

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