In traditional medicine, a doctor decides which medicine or treatment method he or she prescribes based on the results of medical studies. However, there are major objections to these studies. There is often no evidence that the medicine or treatment method is suitable for the individual patient. The results are based on averages. These averages are based on the averages of the group of test subjects. Often these are healthy white men. This is not an average of the population. Children, women, the elderly and people from various ethnic backgrounds are often not taken into account. Also not with people with multiple medical conditions at the same time. Moreover, if studies are repeated, a different result is obtained. So how should doctors trust the advice from the studies? And how does a patient get the treatment that suits him or her? In the Saturday appendix of the NRC of 1 June 2019, there is an article entitled "In the midst of the transition to a new medicine" with the subtitle "Technology as a driver of care of the future". The transition to which this article refers is a transition from medicine based on trial-and-error to medicine based on systems. The idea is that thanks to new technologies, big data and artificial intelligence, a clearer picture can be created of the individual patient and the optimum for this patient.
Pacmed is a company engaged in the development of personal and precise care. To achieve this they develop so-called decision support tools. On the basis of large amounts of data, these tools can help physicians to choose the right treatment for a particular disorder in a particular patient. In our profile paper, we link up with these new ways of thinking. This allows us to combine our interest in medicine and computer science. We focus on urinary tract infections. These are common disorders. We want to investigate whether the analysis of big data can help to offer tailor-made medication and treatments to the individual patient.