Evidence map›Paper›PMID 27499677›Full record

ArticleHealth, risk & society2016

Accounting for failure: risk-based regulation and the problems of ensuring healthcare quality in the NHS.

Anne-Laure Beaussier, David Demeritt, Alex Griffiths, Henry Rothstein

Abstract read
In one paragraph

Article in Health, risk & society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
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  7. Review
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

4 authors.

Anne-Laure BeaussierDepartment of Geography, King's College London , London , UK.
David DemerittDepartment of Geography, King's College London , London , UK.
Alex GriffithsSchool of Management and Business, King's College London , London , UK.
Henry RothsteinDepartment of Geography, King's College London , London , UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we examine why risk-based policy instruments have failed to improve the proportionality, effectiveness, and legitimacy of healthcare quality regulation in the National Health Service (NHS) in England. Rather than trying to prevent all possible harms, risk-based approaches promise to rationalise and manage the inevitable limits of what regulation can hope to achieve by focusing regulatory standard-setting and enforcement activity on the highest priority risks, as determined through formal assessments of their probability and consequences. As such, risk-based approaches have been enthusiastically adopted by healthcare quality regulators over the last decade. However, by drawing on historical policy analysis and in-depth interviews with 15 high-level UK informants in 2013-2015, we identify a series of practical problems in using risk-based policy instruments for defining, assessing, and ensuring compliance with healthcare quality standards. Based on our analysis, we go on to consider why, despite a succession of failures, healthcare regulators remain committed to developing and using risk-based approaches. We conclude by identifying several preconditions for successful risk-based regulation: goals must be clear and trade-offs between them amenable to agreement; regulators must be able to reliably assess the probability and consequences of adverse outcomes; regulators must have a range of enforcement tools that can be deployed in proportion to risk; and there must be political tolerance for adverse outcomes.

Indexed as

Care Quality Commissionhealthcare qualityNational Health Service (NHS)regulation and governancerisk

Identifiers

PMID27499677
PMCPMC4950452

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.