Tierärztliche Fakultät - Digitale Hochschulschriften der LMU - Teil 05/07 Ludwig-Maximilians-Universität München

The focus of this work was the investigation of the following questions in current research: How can one define an objectively verifiable procedure for the veterinarian during the diagnostic process and how can this procedure be
represented at best? Is it possible to validate this procedure in a general way? Are the answers to these previous questions appropriate to lead to more effective and
efficient diagnoses in veterinary medicine?
Symptoms which are relevant for the practical work in the small animal medicine branches dermatology, cardiology and neurology, were divided into sub-groups(rule outs) following a fixed concept which is based on clearly defined criteria. By utilizing this approach, a procedure for the veterinarian is proposed. This systematic framework of the necessary knowledge for finding a diagnosis was
visualized by the author in the form of decision trees which were saved on power point slides. Subsequently, these decision trees were validated qualitatively by
interviews and discussions with professors and senior lecturers of the Clinic for Small Animal Medicine of the LMU Munich. In a next step, this diagnostic support tool was evaluated by the author with students utilizing four tests and a questionnaire. The evaluation of these tests
showed that the students who were allowed to use the decision trees, reached higher scores and needed less time to solve the questions. These results support the thesis that the decision trees are appropriate to increase the efficiency and effectiveness and, in consequence, are a positive contribution to solving clinical cases.
Compared to other diagnostic support tools (some of these have only a limited practicability as support tools), the proposed decision trees can convey detailed knowledge in categorized, structured and visualized form to veterinarians and students of veterinary medicine, so that this tool represents a contribution to finding a diagnosis in a more structured and objective way. In consequence,
diagnostic decisions will be safer, which is especially important for inexperienced veterinarians and veterinary students. The generated decision trees can be utilized as a basis for further investigations of symptoms and should stimulate scientific discussions in the research field
"diagnosis". In this context, the creation of a database is conceivable, which could present the contents and more detailed information in a multimedia-based way.