Over the past decade, WHO has issued over 400 maternal and perinatal health recommendations, providing critical evidence-based guidance to healthcare providers and women worldwide on how best to improve the health of women and newborns during pregnancy, childbirth and the postpartum period. WHO, like other organizations that develop and implement guidelines, faces a major challenge in ensuring that all recommendations are up to date and reflect the latest available evidence.
Updating WHO recommendations
There are no rules as to how frequently guidelines should be updated although 5 years is generally considered the norm. For some recommendations, however, where there is strong new evidence that becomes available, failing to update the corresponding recommendation in a timely manner could adversely affect people’s health and well-being. Conversely some recommendations have seen little change in the evidence and therefore do not warrant the use of limited resources to update them.
The article proposes a framework for assessing priority for updating recommendations. This approach could be adapted by other guideline development organisations to facilitate rapid and efficient updating of recommendations.
The “living guidelines” approach has now been successfully applied in the rapid creation and updating of 25 WHO maternal and perinatal recommendations, including WHO’s recommendation on tranexamic acid for the treatment of postpartum haemorrhage and WHO’s recommendations on uterotonics for the prevention of postpartum haemorrhage. These recommendations are critical to effective prevention and management of postpartum haemorrhage, one of the leading causes of maternal death worldwide.
Challenges ahead
In ensuring that recommendations are as up-to-date as possible, WHO recognizes the challenge that this may present for countries, particularly where resources are scarce, in terms of adopting and adapting them within national settings. The development of digital tools that can be rapidly updated as well as job aids, checklists, decision support algorithms for example will be critical to facilitate the knowledge transfer and uptake of recommendations by end users.