Evidence map›Paper›PMID 34996811›Full record

ArticleBMJ open quality2022

Understanding challenges of using routinely collected health data to address clinical care gaps: a case study in Alberta, Canada.

Taylor McGuckin, Katelynn Crick, Tyler W Myroniuk, Brock Setchell, Roseanne O Yeung, Denise Campbell-Scherer

Abstract read
In one paragraph

Article in BMJ open quality, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Building the future of ICU care: Is our digital foundation strong enough? A multicentre survey of Australian and New Zealand intensive care units.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025
    Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024
    Article
  17. Review
  18. Article
  19. Article
  20. 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

6 authors.

Taylor McGuckinFaculty of Medicine & Dentistry - Lifelong Learning & Physician Learning Program, University of Alberta, Edmonton, Alberta, Canada.ORCID 0000-0002-8800-6081
Katelynn CrickFaculty of Medicine & Dentistry - Lifelong Learning & Physician Learning Program, University of Alberta, Edmonton, Alberta, Canada.
Tyler W MyroniukPublic Health, University of Missouri, Columbia, Missouri, USA.
Brock SetchellFaculty of Medicine & Dentistry - Lifelong Learning & Physician Learning Program, University of Alberta, Edmonton, Alberta, Canada.
Roseanne O YeungFaculty of Medicine & Dentistry - Lifelong Learning & Physician Learning Program, University of Alberta, Edmonton, Alberta, Canada.
Denise Campbell-SchererFaculty of Medicine & Dentistry - Lifelong Learning & Physician Learning Program, University of Alberta, Edmonton, Alberta, Canada denise.campbell-scherer@ualberta.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-quality data are fundamental to healthcare research, future applications of artificial intelligence and advancing healthcare delivery and outcomes through a learning health system. Although routinely collected administrative health and electronic medical record data are rich sources of information, they have significant limitations. Through four example projects from the Physician Learning Program in Edmonton, Alberta, Canada, we illustrate barriers to using routinely collected health data to conduct research and engage in clinical quality improvement. These include challenges with data availability for variables of clinical interest, data completeness within a clinical visit, missing and duplicate visits, and variability of data capture systems. We make four recommendations that highlight the need for increased clinical engagement to improve the collection and coding of routinely collected data. Advancing the quality and usability of health systems data will support the continuous quality improvement needed to achieve the quintuple aim.

Indexed as

Artificial IntelligenceRoutinely Collected Health DataAlbertaElectronic Health RecordsHumansQuality Improvementdata accuracyhealthcare quality improvementhealth services researchquality improvementquality improvement methodologies

Identifiers

PMID34996811
PMCPMC8744094

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.