Evidence map›Paper›PMID 36860913›Full record

ArticleFrontiers in sociology2023

Undertaking rapid evaluations during the COVID-19 pandemic: Lessons from evaluating COVID-19 remote home monitoring services in England.

Holly Walton, Nadia E Crellin, Manbinder S Sidhu, Chris Sherlaw-Johnson, Lauren Herlitz, Ian Litchfield, Theo Georghiou, Sonila M Tomini, Efthalia Massou, Jo Ellins and 2 more

Abstract read
In one paragraph

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Balancing Trade-Offs in Patient and Public Involvement and Engagement in Rapid Evaluation: Reflections and Lessons.Health expectations : an international journal of public participation in health care and health policy · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Holly WaltonDepartment of Applied Health Research, University College London, London, United Kingdom.
Nadia E CrellinResearch and Policy, The Nuffield Trust, London, United Kingdom.
Manbinder S SidhuHealth Services Management Centre, School of Social Policy, University of Birmingham, Birmingham, United Kingdom.
Chris Sherlaw-JohnsonResearch and Policy, The Nuffield Trust, London, United Kingdom.
Lauren HerlitzDepartment of Applied Health Research, University College London, London, United Kingdom.
Ian LitchfieldInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.
Theo GeorghiouResearch and Policy, The Nuffield Trust, London, United Kingdom.
Sonila M TominiGlobal Business School for Health, University College London, London, United Kingdom.
Efthalia MassouDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Jo EllinsHealth Services Management Centre, School of Social Policy, University of Birmingham, Birmingham, United Kingdom.
Jon SussexRAND Europe, Cambridge, United Kingdom.
Naomi J FulopDepartment of Applied Health Research, University College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Rapid evaluations can offer evidence on innovations in health and social care that can be used to inform fast-moving policy and practise, and support their scale-up according to previous research. However, there are few comprehensive accounts of how to plan and conduct large-scale rapid evaluations, ensure scientific rigour, and achieve stakeholder engagement within compressed timeframes. Methods: Using a case study of a national mixed-methods rapid evaluation of COVID-19 remote home monitoring services in England, conducted during the COVID-19 pandemic, this manuscript examines the process of conducting a large-scale rapid evaluation from design to dissemination and impact, and reflects on the key lessons for conducting future large-scale rapid evaluations. In this manuscript, we describe each stage of the rapid evaluation: convening the team (study team and external collaborators), design and planning (scoping, designing protocols, study set up), data collection and analysis, and dissemination. Results: We reflect on why certain decisions were made and highlight facilitators and challenges. The manuscript concludes with 12 key lessons for conducting large-scale mixed-methods rapid evaluations of healthcare services. We propose that rapid study teams need to: (1) find ways of quickly building trust with external stakeholders, including evidence-users; (2) consider the needs of the rapid evaluation and resources needed; (3) use scoping to ensure the study is highly focused; (4) carefully consider what cannot be completed within a designated timeframe; (5) use structured processes to ensure consistency and rigour; (6) be flexible and responsive to changing needs and circumstances; (7) consider the risks associated with new data collection approaches of quantitative data (and their usability); (8) consider whether it is possible to use aggregated quantitative data, and what that would mean when presenting results, (9) consider using structured processes & layered analysis approaches to rapidly synthesise qualitative findings, (10) consider the balance between speed and the size and skills of the team, (11) ensure all team members know roles and responsibilities and can communicate quickly and clearly; and (12) consider how best to share findings, in discussion with evidence-users, for rapid understanding and use. Conclusion: These 12 lessons can be used to inform the development and conduct of future rapid evaluations in a range of contexts and settings.

Indexed as

COVID-19key lessonsmixed methodsrapid evaluationreflections

Identifiers

PMID36860913
PMCPMC9969845

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.