ArticleFrontiers in sociology2023
Very rapid insight generation to support UK health and care systems: An AHSN approach.
Article in Frontiers in sociology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 1 citations in OpenAlex.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: COVID-19 challenges are well documented. Academic Health Science Networks (AHSNs) are a key partner to NHS and care organizations. In response to managing COVID-19 challenges, Wessex AHSN offered rapid insight generation and rapid evaluation to local NHS and care systems to capture learning during this period. This novel "Rapid Insight" approach involved one-off online deliberative events with stakeholders to generate insights linked to specific, priority areas of interest, followed by rapid analysis and dissemination of the findings. Context: Key objectives were to enable system leaders to build their adaptive leadership capability and learn from the experience of COVID-19 to inform recovery planning and system support. Rapid Insight (RI) gathered together health and care professionals into a tightly managed, virtual forum to share system intelligence. Approach: Focused questions asked about the systems' response to the pandemic, what changes to continue and sustain, or discontinue. Participants responded simultaneously to each question using the virtual chat function. Immediate thematic analysis of the chat conducted in 48-72 h by paired analysts for each question strengthened analytical integrity. Mind maps, the key output, provided easily assimilated information and showed linkages between themes. Telephone or virtual interviews of key informants (health and care professionals and patients) and routinely collected data were synthesized into short reports alongside several RI events. However, insufficient time limited the opportunities to engage diverse participants (e.g., mental health users). Data from RI can scope the problem and immediate system needs, to stimulate questions for future evaluative work. Impact: RI facilitated a shared endeavor to discover "clues in the system" by including diverse opinions and experience across NHS and care organizations. Although these rapid virtual events saved on travel time, digital exclusion might constrain participation for some stakeholders which needs other ways to ensure inclusion. Successful rapid engagement required Wessex AHSN's existing system relationships to champion RI and facilitate participant recruitment. RI events "opened the door" to conversations between up to 150 multi-professional clinicians to share their collective response to COVID-19. This paper focuses on the RI approach with a case example and its further development.
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.