Supplement evidence from RCTs with real-world evidence about effectivity and safety of pharmacotherapy. Especially for patients who are often excluded from RCTs, like patients suffering from multiple (chronic) diseases.

Provide reliable computerised monitoring tools for Adverse Drug Events to guide pharmacotherapy improvement projects. This to optimise the plan-do-check-act cycle and establish a real-time and continuous feedback loop.

Develop and validate tools to create high-quality real-world data that can be trusted, upon which robust prediction models can be developed, able to provide actionable and trustworthy advices about pharmacotherapy at point-of-care. For the prediction models various methods are explored, including machine learning. 

Provide easy-to-use clinical decision support software (rule-based or data-driven) to support physicians, pharmacists and patients in safe and effective medication use and monitoring. We aim for software that aligns with end-user needs, fits into their cognitive & information flows, and enhances human-computer interaction.