The treatment of colorectal cancer (CRC) and in particular metastatic CRC (mCRC) has become more complex in the last decades. This has resulted in improved survival rates, although only a subgroup of mCRC patients appears to benefit from the availability of new treatment strategies. This thesis emphasizes the value of population-based data for quality improvement purposes. Continuous monitoring of real-world data provides insight into treatment patterns and practice variation, guideline implementation and guideline (non)adherence, and facilitates future hospital audits. Population-based studies will improve treatment strategies in addition to results derived from clinical trials. The complexity of the treatment of CRC is likely to further increase in the coming decades due to the development of new systemic therapy agents, new options for local treatment of metastases and the integration of molecular and genetic features in the treatment of CRC. This urges the need for new methods to optimize the update process and application of clinical practice guidelines and by integrating guideline recommendations in multidisciplinary tumor boards and during outpatient clinic visits. The implementation of (digital) decision support systems may contribute to the reduction of undesirable practice variation, provide insight in guideline (non)adherence, and may facilitate shared decision-making from patients’ perspective, which will play an increasingly prominent role in the ongoing expansion of treatment options. Ideally, population-based data from past and current CRC patients will be integrated into decision-making for future CRC patients, which contributes to a learning healthcare system, leading to tailored data-driven treatment and optimization of (colorectal) cancer care.