The aim of this project was to investigate what information is critical to convey to nurses when performing digital triage. In addition, the project aimed to investigate how such information could be visualized. This was done through a combined user research and machine learning approach, which enabled for a more nuanced and thorough investigation compared to only making use of one of the two fields. There is sparse research investigating how digital triaging can be improved and made more efficient. Therefore, this study has contributed with new and relevant insights. Three machine learning algorithms were implemented to predict the right level of care for a patient. Out of these three, the random forest classifier proved to have the best performance with an accuracy of 69.46%, also having the shortest execution time. Evaluating the random forest classifier, the most important features were stated to be the duration and progress of the symptoms, allergies to medicine, chronic diseases and the patient's own estimation of his/her health. These factors could all be confirmed by the user research approach, indicating that the results from the approaches were aligned. The results from the user research approach also showed that the patients' own description of their symptoms was of great importance. These findings served as a basis for a number of visualization decisions, aiming to make the triage process as accurate and efficient as possible.