Evidence map›Paper›PMID 39832840›Full record

ReviewBMJ quality & safety2025

A realist review of how, why, for whom and in which contexts quality improvement in healthcare impacts inequalities.

Lucy Lara Johnson, Geoff Wong, Isla Kuhn, Graham P Martin, Anuj Kapilashrami, Laura Lennox, Georgia Bell Black, Matthew Hill, Ryan Swiers, Hashum Mahmood and 3 more

Abstract readReview
In one paragraph

Review in BMJ quality & safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Lucy Lara JohnsonQueen Mary University of London Wolfson Institute of Population Health, London, UK.ORCID http://orcid.org/0000-0002-9272-4668
Geoff WongPrimary Care Health Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-5384-4157
Isla KuhnTHIS Institue, University of Cambridge, Cambridge, UK.
Graham P MartinTHIS Institute, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0003-1979-7577
Anuj KapilashramiSchool of Health and Social Care, University of Essex, Colchester, UK.
Laura LennoxCLAHRC for Northwest London, London, UK.ORCID http://orcid.org/0000-0002-8991-1702
Georgia Bell BlackQueen Mary University of London Wolfson Institute of Population Health, London, UK.ORCID http://orcid.org/0000-0003-2676-5071
Matthew HillQ Community, The Health Foundation, London, UK.
Ryan SwiersSouth Tyneside and Sunderland NHS Foundation Trust, South Shields, UK.
Hashum MahmoodNHS Confederation, London, UK.
Linda JonesPatient and Public Involvement Representative, Sheffield, UK.
Jude BengPatient and Public Involvement Representative, Sheffield, UK.
John FordQueen Mary University of London Wolfson Institute of Population Health, London, UK j.a.ford@qmul.ac.uk.ORCID http://orcid.org/0000-0001-8033-7081

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionQuality improvement (QI) is aimed at improving care. Equity is one of the six domains of healthcare quality, as defined by the Institute of Medicine. If this domain is ignored, QI projects have the potential to maintain or even worsen inequalities. AIMS AND

objectivesWe aimed to understand why, how, for whom and in which contexts QI approaches increase, or do not change health inequalities in healthcare organisations.

methodsWe conducted a realist review by first developing an initial programme theory, then searching MEDLINE, Embase, CINAHL, PsychINFO, Web of Science and Scopus for QI projects that considered health inequalities. Included studies were analysed to generate context-mechanism-outcome configurations (CMOCs) and develop an overall programme theory.

resultsWe screened 6259 records. Thirty-six records met our inclusion criteria, the majority of which were from the USA. We developed CMOCs covering four clusters: values and understanding, resources, data, and design. Five of these described circumstances in which QI may increase inequalities and 15 where it may reduce inequalities. We found that QI projects that are values-led and incorporate diverse, patient-led data into design are more likely to address health inequalities. However, when staff and patients cannot engage fully with equity-focused projects, due to practical or technological barriers, QI projects are more likely to worsen inequalities.

conclusionsThe potential for QI projects to positively impact inequalities depends on embedding equity-focused values across organisations, ensuring sufficient and appropriate resources are provided to staff delivering QI, and using diverse disaggregated data alongside considered user involvement to inform and assess the success of QI projects. Policymakers and practitioners should ensure that QI projects are used to address inequalities.

Indexed as

Healthcare DisparitiesQuality ImprovementHumansQuality of Health CareHealthcare quality improvementHealth policyQuality improvement

Identifiers

PMID39832840
PMCPMC12322391

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.