In healthcare, the path from diagnosing diseases to effective treatment is often long and costly. On the one hand, waiting times have increased significantly in the past years. On the other hand, insufficient or suboptimal testing has the risk of misdiagnosis, which can lead to permanent disabilities or death. Furthermore, many diagnostic test paths fail to consider a patients’ medical history, leading to unnecessarily many tests, creating unnecessary medical waste and increasing the risk of side-effects. Once the disease of a patient is diagnosed, treatment of the disease must be administered at the right personalized dosage levels, otherwise the treatment might not work, or may even lead to harmful side-effects of overdoses.
My research aims to address these challenges through three interconnected studies. The first study investigates the trade-off between improving diagnostic accuracy and reducing congestion, where clinicians can increase the test speed against higher costs. The second study explores whether taking a patients’ medical history into account can shorten the time-to-diagnosis. The third study focusses on optimizing individual treatment dosages by leveraging population-level data on medication effects.
