Spotlight on community engagement: NICE resources in the context of COVID-19-NICE public health guidance update.

This article focuses on how the National Institute for Health and Care Excellence (NICE) quality standard on 'Community engagement: improving health and wellbeing' (QS148) may be used to support local areas with pandemic recovery planning. This article sets the standard in the context of the coronavirus pandemic, explores some of its content and highlights additional NICE resources to support its use across the health and care system.

NICE update NICE public health guidance update.

This article covers recently published National Institute for Health and Care Excellence (NICE) guidance relevant to public health and a review of evidence published since we released the NICE guidance on unintentional injury prevention in under 15 year olds. The article features some of this evidence that was found to reinforce published recommendations on safety in the home and on the roads, indicating the guidance remains up to date and relevant. In addition, it importantly highlights that there is great opportunity to prevent future unintentional injury through integrated and coordinated, evidence and intelligence-informed approaches.

Reaching a shared understanding of shared decision making in health care: NICE's experience of scoping the shared decision making guideline.

NICE's guideline on shared decision making, currently under development, endeavours to support shared decision making as part of routine health care practice. In this article, we summarize our learning to date, gained through the scoping of the guideline, on the key challenges that need to be addressed in the guideline. The production of a scope is the first stage in the development of a NICE guideline, setting the parameters for what will be considered in the guideline.

Prioritising references for systematic reviews with RobotAnalyst: A user study.

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process.