Pharmacist-led collaborative medication management programs for oral antineoplastic therapies: A systematic...

The aim of this systematic review is to summarize the structure, process, and outcomes of pharmacist-led collaborative medication management programs for oral antineoplastic therapies (OATs).

Included studies were peer-reviewed journal articles published in English, between January 2000 to May 2020, and reporting on pharmacist-led collaborative medication management programs for patients on OATs. To be included, studies had to report on the pharmacy practice model, pharmacist interventions, and outcomes of the medication management program. The Donabedian model informed the data extraction and summary. Two independent researchers assessed the risk of bias (confounding) for all included studies (n = 12) using the NIH tool and Cochrane ROBINS-I for observational research.

There were 12 studies that met inclusion criteria. The structure of the programs included hiring oncology pharmacists to deliver interventions, standardized templates for electronic medical record documentation, and administrative workflow changes (e.g., automatic referrals). The most common pharmacist interventions (processes) were patient education and counseling, adverse event monitoring, and dose modifications. All studies reported one or more positive outcomes, including improved patient adherence, safety, cost savings, cost avoidance, and patient satisfaction. All included studies used an observational study designs, and the majority of studies had moderate to high risk of bias.

The evidence suggests that pharmacist-led collaborative medication management programs may have beneficial clinical and economic outcomes. The implementation of these programs could be strengthened by using a conceptual framework to guide program development, implementation, and evaluation and effectiveness-hybrid study designs to assess clinical and implementation outcomes. The risk of bias should be addressed by using more robust study designs and rigorous data collection and analysis methods.