Evidence map›Paper›PMID 28651340›Full record

ArticleInternational journal for quality in health care : journal of the International Society for Quality in Health Care2017

Linking quality indicators to clinical trials: an automated approach.

Enrico Coiera, Miew Keen Choong, Guy Tsafnat, Peter Hibbert, William B Runciman

Abstract read
In one paragraph

Article in International journal for quality in health care : journal of the International Society for Quality in Health Care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Does health informatics have a replication crisis?Journal of the American Medical Informatics Association : JAMIA · 2018
    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

5 authors.

Enrico CoieraCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.
Miew Keen ChoongCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.
Guy TsafnatCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.
Peter HibbertCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.
William B RuncimanCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveQuality improvement of health care requires robust measurable indicators to track performance. However identifying which indicators are supported by strong clinical evidence, typically from clinical trials, is often laborious. This study tests a novel method for automatically linking indicators to clinical trial registrations.

designA set of 522 quality of care indicators for 22 common conditions drawn from the CareTrack study were automatically mapped to outcome measures reported in 13 971 trials from ClinicalTrials.gov.

interventionText mining methods extracted phrases mentioning indicators and outcome phrases, and these were compared using the Levenshtein edit distance ratio to measure similarity.

main outcome measureNumber of care indicators that mapped to outcome measures in clinical trials.

resultsWhile only 13% of the 522 CareTrack indicators were thought to have Level I or II evidence behind them, 353 (68%) could be directly linked to randomized controlled trials. Within these 522, 50 of 70 (71%) Level I and II evidence-based indicators, and 268 of 370 (72%) Level V (consensus-based) indicators could be linked to evidence. Of the indicators known to have evidence behind them, only 5.7% (4 of 70) were mentioned in the trial reports but were missed by our method.

conclusionsWe automatically linked indicators to clinical trial registrations with high precision. Whilst the majority of quality indicators studied could be directly linked to research evidence, a small portion could not and these require closer scrutiny. It is feasible to support the process of indicator development using automated methods to identify research evidence.

Indexed as

Quality Indicators, Health CareRandomized Controlled Trials as TopicData MiningHumansOutcome Assessment, Health Careclinical trialsconcept mappingindicatorquality of health caretext mining

Identifiers

PMID28651340
PMCPMC5890874

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.