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Almost one in five individuals will experience a depressive disorder during their life. Apart from the impact on patients’ lives, depressive disorders also have a considerable impact on society. Despite the availability of various evidence-based treatments for depression, most treatments are only moderately effective. This can lead to a long journey to find a suitable treatment. In her dissertation, Kaying Kan focuses on improving care for patients with depression by learning from past and current treatment practices. For this purpose, routinely collected mental healthcare data regarding patient characteristics, treatment and outcomes were used. Her studies reveal that the effectiveness of treatments using an algorithm-based care program for depression in clinical practice may verge on results obtained in randomized controlled trials. One study demonstrated that treatment costs of patients with a depression and a personality disorder were considerably higher compared with treatment costs of patients with depression and other psychiatric comorbidities. Importantly, it was also shown that patients and clinicians consider restoring social functioning and achieving personal goals relevant outcomes of treatment. However, patients make a clear distinction between outcomes in first versus recurrent depressions. Furthermore, the development of a data-driven decision-aid for depression to enhance shared decision-making was presented, and an alternative approach for healthcare priority setting, in which different stakeholders systematically assess cost-effective treatments on aspects like feasibility and acceptability. The studies described in the dissertation demonstrate the potential of using linked administrative data enriched with qualitative data for improving care for patients with depression.

Almost one in five individuals will experience a depressive disorder during their life. Apart from the impact on patients’ lives, depressive disorders also have a considerable impact on society. Despite the availability of various evidence-based treatments for depression, most treatments are only moderately effective. This can lead to a long journey to find a suitable treatment. In her dissertation, Kaying Kan focuses on improving care for patients with depression by learning from past and current treatment practices. For this purpose, routinely collected mental healthcare data regarding patient characteristics, treatment and outcomes were used. Her studies reveal that the effectiveness of treatments using an algorithm-based care program for depression in clinical practice may verge on results obtained in randomized controlled trials. One study demonstrated that treatment costs of patients with a depression and a personality disorder were considerably higher compared with treatment costs of patients with depression and other psychiatric comorbidities. Importantly, it was also shown that patients and clinicians consider restoring social functioning and achieving personal goals relevant outcomes of treatment. However, patients make a clear distinction between outcomes in first versus recurrent depressions. Furthermore, the development of a data-driven decision-aid for depression to enhance shared decision-making was presented, and an alternative approach for healthcare priority setting, in which different stakeholders systematically assess cost-effective treatments on aspects like feasibility and acceptability. The studies described in the dissertation demonstrate the potential of using linked administrative data enriched with qualitative data for improving care for patients with depression.