Access to essential medicines is a key component of Universal Health Coverage, and the World Health Organization‘s Model List of Essential Medicine (WHO EML) has played a critical role in guiding the country-level selection and financing of medicines for more than four decades. This study identified factors affecting adaptation and implementation of the WHO Model List of Essential Medicines (WHO EML) at the national level.

A qualitative evidence synthesis was conducted, by searching ten databases through October 2021. Primary qualitative studies focused on country-level implementation of the WHO EML were included. We screened 1,567 unique citations, reviewed 183 full texts, and included 23 studies, from 30 settings. Our findings centered around three main ideas pertaining to national adaptation and implementation of the WHO EML: (1) the importance of designing institutions, governance and leadership for national medicines lists, particularly the consideration of transparency, leadership, legislative mechanisms, managing regional differences and clinical guidance; (2) the capacity to manage evidence to inform NML updates, including processes for contextualizing the global evidence, utilizing local data and expert knowledge and assessing costs, to which locally relevant data and the use of cost-effectiveness information plays an important role; (3) the influence of NML on purchasing and prescribing by altering provider incentives, through linkages to systems for financing and procurement and donor influence. Non-English studies and experiences and perceptions of stakeholders published in grey literature was not collected.

This qualitative evidence synthesis underscores the complexity and interdependencies inherent to implementation of the WHO EML. To maximize the value of national medicines lists, greater investments should be made in processes and institutions that are needed to support various stages of the implementation pathway. Moreover, further research on linkages between NMLs, procurement and the availability of medicines will provide additional insight into optimal NML implementation.