Evidence map›Paper›PMID 16326793›Full record

ArticleQuality & safety in health care2005

Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality indicators.

B Guthrie, T Love, T Fahey, A Morris, F Sullivan

Abstract readComparative Study
In one paragraph

Article in Quality & safety in health care, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

B GuthrieDepartment of Community Health Sciences, University of Dundee, Dundee DD2 4BF, UK. b.guthrie@chs.dundee.ac.uk
T Love
T Fahey
A Morris
F Sullivan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Summarising the complex data generated by multiple cross sectional quality indicators in a way that patients, clinicians, managers and policymakers find useful is challenging. A common approach is aggregation to create summary measures such as star ratings and balanced score cards, but these may conceal the detail needed to focus quality improvement. We propose an alternative way of summarising and presenting multiple quality indicators, suitable for use for quality improvement and governance. This paper discusses (1) control charts for repeated measurements of single processes as used in industrial statistical process control (SPC); (2) control charts for cross sectional comparison of many institutions for a single quality indicator (rarely used in industry but commonly proposed for health care); and (3) small multiple graphics which combine control chart signal extraction with efficient graphical presentations for multiple indicators.

Indexed as

Data Interpretation, StatisticalQuality Assurance, Health CareQuality Indicators, Health CareAdultChildConfidence IntervalsCross-Sectional StudiesDiabetes Mellitus, Type 2Glycated HemoglobinHumansInfantLongitudinal StudiesPrimary Health CareScotlandTetanus ToxoidGlycated HemoglobinTetanus Toxoid

Identifiers

PMID16326793
PMCPMC1744105

What OpenQuestion holds

Textmetadata
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.