Algorithms and collaborative-care systems for depression: Are they effective and why? A systematic review

Background: Treatment algorithms and collaborative-care systems are systematic treatment approaches that are designed to improve outcomes by enhancing the quality of care. During the last decade. algorithm research has evolved as a new branch of clinical research that evaluates the clinical and economic impact of algorithm-guided treatment in primary and psychiatric care of patients with depressive disorders. Methods: This article discusses the rationale of algorithm development, their risks and limitations, and important elements in their implementation in clinical practice. It further reviews the available studies that have evaluated algorithm-guided treatment for depression. Results: Recent studies show that compared with treatment as usual, the use of algorithms and collaborative-care approaches in the care of depressed patients enhances treatment outcomes by modifying practice procedures and treatment processes. Conclusions: Treatment algorithms and collaborative-care systems clearly increase the efficacy of applied treatments in the care of depressed patients. However, to what extent the enhanced outcomes are a result of diligent measurement-based care or of the specific treatment steps that are used remains to be resolved. Valid clinical or pharmacogenetic predictors of response are needed to further tailor specific algorithms to individual patients.