Evidence map›Paper›PMID 27435950›Full record

ArticleInternational journal of medical informatics2016

A multi-site cognitive task analysis for biomedical query mediation.

Gregory W Hruby, Luke V Rasmussen, David Hanauer, Vimla L Patel, James J Cimino, Chunhua Weng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Gregory W HrubyDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Luke V RasmussenDivision of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
David HanauerDepartment of Pediatrics, University of Michigan, Ann Arbor, MI, USA; School of Information, University of Michigan, Ann Arbor, MI, USA.
Vimla L PatelDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; The New York Academy of Medicine, New York, NY, USA.
James J CiminoDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Informatics Institute in School of Medicine, University of Alabama, Birmingham, AL, USA.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address: chunhua@columbia.edu.

Funding

Northwestern University Clinical and Translational Science Institute (NUCATS)UL1TR001422 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI D'AQUILA, RICHARD · 2015 to 2023
$56.8M
Michigan Institute for Clinical and Health Research (MCHR)UL1TR000433 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$49.9M
Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR000433NCATS NIH HHS UL1 TR001422NLM NIH HHS R01 LM009886NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

objectiveTo apply cognitive task analyses of the Biomedical query mediation (BQM) processes for EHR data retrieval at multiple sites towards the development of a generic BQM process model. MATERIALS AND

methodsWe conducted semi-structured interviews with eleven data analysts from five academic institutions and one government agency, and performed cognitive task analyses on their BQM processes. A coding schema was developed through iterative refinement and used to annotate the interview transcripts. The annotated dataset was used to reconstruct and verify each BQM process and to develop a harmonized BQM process model. A survey was conducted to evaluate the face and content validity of this harmonized model.

resultsThe harmonized process model is hierarchical, encompassing tasks, activities, and steps. The face validity evaluation concluded the model to be representative of the BQM process. In the content validity evaluation, out of the 27 tasks for BQM, 19 meet the threshold for semi-valid, including 3 fully valid: "Identify potential index phenotype," "If needed, request EHR database access rights," and "Perform query and present output to medical researcher", and 8 are invalid. DISCUSSION: We aligned the goals of the tasks within the BQM model with the five components of the reference interview. The similarity between the process of BQM and the reference interview is promising and suggests the BQM tasks are powerful for eliciting implicit information needs.

conclusionsWe contribute a BQM process model based on a multi-site study. This model promises to inform the standardization of the BQM process towards improved communication efficiency and accuracy.

Indexed as

CognitionDatabases, FactualInformation Storage and RetrievalTask Performance and AnalysisCommunicationData MiningHumansMedical Informatics ComputingUser-Computer InterfaceElectronic health recordsInformation storage and retrievalTask performance and analysis

Identifiers

PMID27435950
PMCPMC4957698

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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.