Evidence map›Paper›PMID 33709066›Full record

ArticleJAMIA open2021

An electronic health record (EHR) log analysis shows limited clinician engagement with unsolicited genetic test results.

Jordan G Nestor, Alexander Fedotov, David Fasel, Maddalena Marasa, Hila Milo-Rasouly, Julia Wynn, Wendy K Chung, Ali Gharavi, George Hripcsak, Suzanne Bakken and 2 more

Open access · goldAbstract readCase Reports
In one paragraph

Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
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

8 citing papers in PubMed, 11 citations in OpenAlex.

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  8. Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021
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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

12 authors at 1 institution in 1 country.

Jordan G NestorDepartment of Medicine, Division of Nephrology, Columbia University, New York, New York, USA.ORCID https://orcid.org/0000-0003-1418-3103
Alexander FedotovThe Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA.
David FaselDepartment of Medicine, Center for Precision Medicine and Genomics, Columbia University, New York, New York, USA.
Maddalena MarasaDepartment of Medicine, Division of Nephrology, Columbia University, New York, New York, USA.
Hila Milo-RasoulyDepartment of Medicine, Division of Nephrology, Columbia University, New York, New York, USA.
Julia WynnDepartment of Pediatrics, Columbia University, New York, New York, USA.
Wendy K ChungDepartments of Pediatric and Medicine, Columbia University, New York, New York, USA.
Ali GharaviDepartment of Medicine, Division of Nephrology, Columbia University, New York, New York, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Suzanne BakkenDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Soumitra SenguptaDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Columbia University · US

Funding

NRSA Training CoreTL1TR001875 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SPANN, MARISA N, TAYLOR, JACQUELYN Y · 2016 to 2025
$8.9M
NCATS NIH HHS TL1 TR001875
6 · The paper itself

Abstract

How clinicians utilize medically actionable genomic information, displayed in the electronic health record (EHR), in medical decision-making remains unknown. Participating sites of the Electronic Medical Records and Genomics (eMERGE) Network have invested resources into EHR integration efforts to enable the display of genetic testing data across heterogeneous EHR systems. To assess clinicians' engagement with unsolicited EHR-integrated genetic test results of eMERGE participants within a large tertiary care academic medical center, we analyzed automatically generated EHR access log data. We found that clinicians viewed only 1% of all the eMERGE genetic test results integrated in the EHR. Using a cluster analysis, we also identified different user traits associated with varying degrees of engagement with the EHR-integrated genomic data. These data contribute important empirical knowledge about clinicians limited and brief engagements with unsolicited EHR-integrated genetic test results of eMERGE participants. Appreciation for user-specific roles provide additional context for why certain users were more or less engaged with the unsolicited results. This study highlights opportunities to use EHR log data as a performance metric to more precisely inform ongoing EHR-integration efforts and decisions about the allocation of informatics resources in genomic research.

Indexed as

clinical engagement with genomic resultselectronic health recordslog analysis

Identifiers

PMID33709066
PMCPMC7935499
OpenAlexW3134987839

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.