Objectives Non-medical switching (NMS) strategies have the capacity to reduce overall costs in hospitals while maintaining a high level of care. However, the most appropriate diseases and/or medicines for NMS strategies are still vague. The aim of this review was to give a state-of-the-art summary regarding the economic outcomes resulting from the use of NMS strategies and to discuss whether they would be implementable in a hospital inpatient setting.

Methods A systematic literature search was conducted in Medline, Embase, and ScienceDirect. Studies published between 1988 and 2018 were included if they evaluated the economic impact of NMS strategies or if they performed an economic evaluation between two drugs. Studies regarding antineoplastic agents, endocrine therapies, and immunostimulants, or immunosuppressants, and biosimilars were excluded.

Results Fifty (69%) studies assessing an NMS strategy and 22 (31%) studies comparing two medicines were allocated to four categories: prospective studies (n=8, 11%); retrospective chart reviews (n=29, 40%); retrospective claims analysis (n=13, 18%); and retrospective data analysis (n=22, 31%). Hypercholesterolemia, peptic ulcer, and gastro-oesophageal reflux diseases, diabetes mellitus, and venous thromboembolism were the most prevalent diseases in studies evaluating an NMS strategy. Sixty-eight per cent of the included papers reported a reduction in costs with no significant changes in health outcomes and 8 per cent reported a deterioration in health outcomes and/or increased costs.

Conclusion Regardless of the exclusion of studies regarding biologics or medicines used in oncology, the review highlights that NMS strategies with medicines whose management do not require a thorough clinical assessment were associated with reduced costs and no significant changes in patients’ health outcomes, in the inpatient setting. NMS strategies targeting medicines that require an extensive clinical assessment should be evaluated using hospital-specific effectiveness and/or utility data prior to their implementation.