Evidence map›Paper›PMID 35171102›Full record

ArticleJournal of medical Internet research2022

Habit and Automaticity in Medical Alert Override: Cohort Study.

Le Wang, Kim Huat Goh, Adrian Yeow, Hermione Poh, Ke Li, Joannas Jie Lin Yeow, Gamaliel Tan, Christina Soh

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Interruptive Electronic Alerts for Choosing Wisely Recommendations: A Cluster Randomized Controlled Trial.Journal of the American Medical Informatics Association : JAMIA · 2022
    Trial
  2. Article
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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

8 authors at 5 institutions in 2 countries.

Le WangCity University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-0375-3177
Kim Huat GohNanyang Technological University, Singapore, Singapore.ORCID 0000-0001-7327-0106
Adrian YeowSingapore University of Social Sciences, Singapore, Singapore.ORCID 0000-0003-1355-8540
Hermione PohMedical Informatics, National University Health System, Singapore, Singapore.ORCID 0000-0003-1721-0840
Ke LiMedical Informatics, National University Health System, Singapore, Singapore.ORCID 0000-0002-2588-8240
Joannas Jie Lin YeowMedical Informatics, National University Health System, Singapore, Singapore.ORCID 0000-0001-6622-2288
Gamaliel TanMedical Informatics, National University Health System, Singapore, Singapore.ORCID 0000-0003-2384-2350
Christina SohNanyang Technological University, Singapore, Singapore.ORCID 0000-0003-0307-3566
National University Health System · SGNanyang Technological University · SGCity University of Hong Kong · HKNg Teng Fong General Hospital · SGSingapore University of Social Sciences · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrior literature suggests that alert dismissal could be linked to physicians' habits and automaticity. The evidence for this perspective has been mainly observational data. This study uses log data from an electronic medical records system to empirically validate this perspective.

objectiveWe seek to quantify the association between habit and alert dismissal in physicians.

methodsWe conducted a retrospective analysis using the log data comprising 66,049 alerts generated from hospitalized patients in a hospital from March 2017 to December 2018. We analyzed 1152 physicians exposed to a specific clinical support alert triggered in a hospital's electronic medical record system to estimate the extent to which the physicians' habit strength, which had been developed from habitual learning, impacted their propensity toward alert dismissal. We further examined the association between a physician's habit strength and their subsequent incidences of alert dismissal. Additionally, we recorded the time taken by the physician to respond to the alert and collected data on other clinical and environmental factors related to the alerts as covariates for the analysis.

resultsWe found that a physician's prior dismissal of alerts leads to their increased habit strength to dismiss alerts. Furthermore, a physician's habit strength to dismiss alerts was found to be positively associated with incidences of subsequent alert dismissals after their initial alert dismissal. Alert dismissal due to habitual learning was also found to be pervasive across all physician ranks, from junior interns to senior attending specialists. Further, the dismissal of alerts had been observed to typically occur after a very short processing time. Our study found that 72.5% of alerts were dismissed in under 3 seconds after the alert appeared, and 13.2% of all alerts were dismissed in under 1 second after the alert appeared. We found empirical support that habitual dismissal is one of the key factors associated with alert dismissal. We also found that habitual dismissal of alerts is self-reinforcing, which suggests significant challenges in disrupting or changing alert dismissal habits once they are formed.

conclusionsHabitual tendencies are associated with the dismissal of alerts. This relationship is pervasive across all levels of physician rank and experience, and the effect is self-reinforcing.

Indexed as

Decision Support Systems, ClinicalMedical Order Entry SystemsPhysiciansCohort StudiesElectronic Health RecordsHabitsHumansRetrospective Studiesalert systemselectronic medical recordhabitshealth personnel alert fatigue

Identifiers

PMID35171102
PMCPMC8892274
OpenAlexW4212795577

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.