Evidence map›Paper›PMID 23626913›Full record

ArticleHealthcare informatics research2013

Informatics as tool for quality improvement: rapid implementation of guidance for the management of chronic kidney disease in England as an exemplar.

Simon de Lusignan

Abstract read
In one paragraph

Article in Healthcare informatics research, 2013. 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. Article
  2. Article
  3. Article
  4. 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

1 author.

Simon de LusignanDepartment of Health Care Management and Policy, University of Surrey, Guildford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesChronic kidney disease (CKD) is an important cause of excess cardiovascular mortality and morbidity; as well as being associated with progression to end stage renal disease. This condition was largely unheard of in English primary care prior to the introduction of pay-for-performance targets for management in 2006. A realist review of how informatics has been a mechanism for national implementation of guidance for the improved management of CKD.

methodsRealist review of context, the English National Health Service with a drive to implement explicit national quality standards; mechanism, the informatics infrastructure and its alignment with policy objectives; and outcomes are describe at the micro-data and messaging, meso-patient care and quality improvement initiatives, and marco-national policy levels.

resultsAt the micro-level computerised medical records can be used to reliably identify people with CKD; though differences in creatinine assays, fluctuation in renal function, and errors in diabetes coding were less well understood. At the meso-level more aggressive management of blood pressure (BP) in individual patients appears to slow or reverse decline in renal function; technology can support case finding and quality improvement at the general practice level. At the macro-level informaticians can help ensure that leverage from informatics is incorporated in policy, and ecological investigations inform if there is any association with improved health outcomes.

conclusionsIn the right policy context informatics appears to be an enabler of rapid quality improvement. However, a causal relationship or generalisability of these findings has not been demonstrated.

Indexed as

Computerized Medical Records SystemsDiabetes MellitusHealth PolicyKidney Function TestsMedical InformaticsQuality of Health CareRenal Insufficiency

Identifiers

PMID23626913
PMCPMC3633175

What OpenQuestion holds

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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.