Evidence map›Paper›PMID 32661721›Full record

ArticleCanadian journal of anaesthesia = Journal canadien d'anesthesie2020

Logging in: a comparative analysis of electronic health records versus anesthesia resident-driven logbooks.

Ryan McGinn, Alexander J Lingley, Daniel I McIsaac, Christopher Pysyk, Meghan C McConnell, Gregory L Bryson, Daniel Dubois

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Article in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

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

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

  1. Pooled it
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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

7 authors.

Ryan McGinnDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada.
Alexander J LingleyFaculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Daniel I McIsaacDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada.
Christopher PysykDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada.
Meghan C McConnellDepartment of Innovation in Medical Education, University of Ottawa, Ottawa, ON, Canada.
Gregory L BrysonDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada.
Daniel DuboisDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada. dadubois@toh.ca.ORCID http://orcid.org/0000-0002-7088-5110

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeResident logbooks (RLBs) documenting clinical case exposure are widespread in medical education despite evidence of poor accuracy. Electronic health records (e.g., anesthesia information management systems [AIMS]) may provide advantages for auditing longitudinal case exposure. We evaluated the agreement between AIMS and RLBs for tracking case exposure during anesthesiology residency.

methodsWe performed a historical cohort study with anesthesiology residents (2011-2018, all of whom used a RLB contemporaneously with AIMS) working in a multisite academic health sciences network. The primary outcome was total case-load logging; secondary outcomes were volumes for seven surgical specialties (general, gynecology, neuro, orthopedic, thoracic, urology, and vascular surgery). Correlation of case numbers tracked by AIMS vs RLB was assessed using Pearson correlation; agreement was determined using Bland-Altman plots and intraclass correlation coefficients (ICC).

resultsData from 27 anesthesiology residents were collected. Overall, mean (standard deviation) case numbers were generally greater with AIMS vs RLB [649 (103) vs 583 (191); P = 0.049). Total case volumes between systems had moderate correlation (r = 0.50) and agreement (intraclass correlation coefficient [ICC], 0.42; 95% CI, 0.34 to 0.59). Bland-Altman plots showed variable agreement between AIMS and RLB data [mean (SD) bias = 66 (166) cases]. For general, gynecology, neuro, orthopedic, thoracic, urology, and vascular surgery, there was a range of poor to moderate agreement (ICC, 0.23-0.57) between AIMS and RLB.

conclusionFor anesthesiology resident case-logging, the number of cases logged in an AIMS was higher with lower variance compared with RLBs. Anesthesia information management systems vs RLB data showed low-moderate correlation and agreement. Given the additional time and resources required for RLBs, AIMS may be a superior method for tracking cases where available.

Indexed as

AnesthesiaAnesthesiologyInternship and ResidencyClinical CompetenceCohort StudiesEducation, Medical, GraduateElectronic Health RecordsHumansanesthesiologyclinical experienceLogbook, competency-based medical education

Identifiers

PMID32661721

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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.