ArticleSystematic reviews2022
Rapid evidence synthesis to enable innovation and adoption in health and social care.
Article in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- [Therapeutic indications in lawsuits seeking medical Cannabis in Brazil: a cross-sectional study in light of scientific evidence].Cadernos de saude publica · 2026Article
- Agile Nudge University Innovation Forum: solving real-world problems.Frontiers in public health · 2025Article
- Virtual wards: a rapid evidence synthesis and implications for the care of older people.Age and ageing · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe rapid identification and adoption of effective innovations in healthcare is a known challenge. The strongest evidence base for innovations can be provided by evidence synthesis, but this is frequently a lengthy process and even rapid versions of this can be time-consuming and complex. In the UK, the Accelerated Access Review and Academic Health Science Network (AHSN) have provided the impetus to develop a consistently rapid process to support the identification and adoption of high-value innovations in the English NHS.
methodsThe Greater Manchester Applied Research Collaboration (ARC-GM) developed a framework for a rapid evidence synthesis (RES) approach, which is highly integrated within the innovation process of the Greater Manchester AHSN and the associated healthcare and research ecosystem. The RES uses evidence synthesis approaches and draws on the GRADE Evidence to Decision framework to provide rapid assessments of the existing evidence and its relevance to specific decision problems. We implemented this in a real-time context of decision-making around adoption of innovative health technologies.
resultsKey stakeholders in the Greater Manchester decision-making process for healthcare innovations have found that our approach is both timely and flexible; it is valued for its combination of rigour and speed. Our RES approach rapidly and systematically identifies, appraises and contextualises relevant evidence, which can then be transparently incorporated into decisions about the wider adoption of innovations. The RES also identifies limitations in existing evidence for innovations and this can inform subsequent evaluations. There is substantial interest from other ARCs and AHSNs in implementing a similar process. We are currently exploring methods to make completed RES publicly available. We are also exploring methods to evaluate the impact of using RES as more implementation decisions are made.
conclusionsThe RES framework we have implemented combines transparency and consistency with flexibility and rapidity. It therefore maximises utility in a real-time decision-making context for healthcare innovations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.