English

A clear understanding of the effectiveness and cost-effectiveness of different treatments in clinical practice is a necessity to be able to continuously improve healthcare. Such an understanding is currently lacking, however, due to a lack of structured registration of the type of treatments that patients are given. As a result, clinical practice cannot meaningfully contribute to the scientific evidence-base concerning the effectiveness and cost-effectiveness of interventions. Unfortunately, randomized controlled trials often do not report beyond the effectiveness and cost-effectiveness of the average patient, due to a lack of a sufficient sample size. Even though a treatment might be effective on average, there will always be patients who do and do not respond to a particular treatment. Increasing our understanding of the type of patients (in terms of age, gender, education, work status, etc) who do and do not respond well to particular treatments could have an enormous impact on the quality of care, as this could increase the overall effectiveness of treatments, reduce unnecessary treatment and help to contain healthcare costs.

Contrary to most randomized trials, the large number of patients treated in clinical practice offers the opportunity to look at subgroups of patients and provide meaningful insights into the type of patients that respond particularly well (or particularly poorly) to specific treatments. This project aims to use the large amounts of data available in clinical practice, complemented with data collected on the type of treatment given, to determine the effectiveness and cost-effectiveness of the different treatments for different types of patients treated in basic mental healthcare in the Netherlands. This novel approach will be the first time administrative data will be used on a large-scale basis to learn about effectiveness and cost-effectiveness of different treatments in clinical practice, and provides a unique opportunity to learn about the right treatment for the right patient.

A better understanding of the right treatment for the right patient makes it possible for healthcare providers to improve their healthcare. This approach is complemented with extensive patient preferences research to optimally guide the process of improving healthcare. With all the right measurements in place (with information being collected on the type of patient, type of treatment, treatment effects and treatment costs), improvement of the healthcare process could automatically be monitored and evaluated. By keeping the additional efforts required to measure the right data to a minimum, this project aims to construct a continuously improving and self-learning healthcare system, which healthcare providers could keep using after the project has been finished.

This project will therefore make it possible for the first time to continuously assess the effectiveness and cost-effectiveness of different types of treatments delivered in practice. This opens up the opportunity to continuously improve healthcare, both in terms of effectiveness and cost-effectiveness, by having a constant flow of patient, treatment and outcome data continuously increasing our understanding on the right treatment for the right patient.

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