Evidence map›Paper›PMID 29320797›Full record

ArticleApplied clinical informatics2018

Measuring Electronic Health Record Use in Primary Care: A Scoping Review.

Michael Z Huang, Candace J Gibson, Amanda L Terry

Abstract readScoping Review
In one paragraph

Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. Primary Care Informatics: Vitalizing the Bedrock of Health Care.Journal of medical Internet research · 2024
    Article
  8. Management of Rheumatoid Arthritis in Primary Care: A Scoping Review.International journal of environmental research and public health · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  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

3 authors.

Michael Z Huang
Candace J Gibson
Amanda L Terry

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimple measures of electronic health record (EHR) adoption may be inadequate to evaluate EHR use; and positive outcomes associated with EHRs may be better gauged when varying degrees of EHR use are taken into account. In this article, we aim to assess the current state of the literature regarding measuring EHR use.

objectiveThis article conducts a scoping review of the literature to identify and classify measures of primary care EHR use with a focus on the Canadian context.

methodsWe conducted a scoping review. Multiple citation databases were searched, as well as gray literature from relevant Web sites. Resulting abstracts were screened for inclusion. Included full texts were reviewed by two authors. Data from the articles were extracted; we synthesized the findings. Subsequently, we reviewed these results with seven EHR stakeholders in Canada.

resultsThirty-seven articles were included. Eighteen measured EHR function use individually, while 19 incorporated an overall level of use. Eight frameworks for characterizing overall EHR use were identified.

conclusionThere is a need to create standardized frameworks for assessing EHR use.

Indexed as

Primary Health CareCanadaData CollectionElectronic Health RecordsHealth Information SystemsHumansMedicaidMedicarePublicationsStakeholder ParticipationUnited States

Identifiers

PMID29320797
PMCPMC5801885

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.