Evidence map›Paper›PMID 32308847›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019

A Potential Answer to the Alert Override Riddle: Using Patient Attributes to Predict False Positive Alerts.

Timothy I Kennell, James J Cimino

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Racial Disparity in Clinical Alert Overrides.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021
    Article
  5. 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

2 authors.

Timothy I KennellInformatics Institute, University of Alabama at Birmingham, Birmingham, AL.
James J CiminoInformatics Institute, University of Alabama at Birmingham, Birmingham, AL.

Funding

UAB Center for Clinical and Translational Science (CCTS)UL1TR001417 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI KIMBERLY, ROBERT P. · 2015 to 2018
$31.4M
NCATS NIH HHS UL1 TR001417
6 · The paper itself

Abstract

Electronic health records (EHRs) use alerts to help prevent medical errors, yet clinicians override many of these alerts due to desensitization from constant exposure (alert fatigue). We hypothesize that a clinician might override an alert warning about the dangers of a treatment if the patient's health is so poor that the treatment is worth the risk or if a patient's health suggests the treatment is not needed. We used logistic regression with general estimating equations to determine if the Early Warning Score (EWS), a measurement used to predict critical care need, could be used to predict alert overrides. EWS was a significant predictor of overrides for three alerts. Although EWS could not predict overrides for all alert rules, these results suggest that EWS may be helpful for some alerts, but that additional EHR data will be needed for predicting override behavior to a useful degree.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsHealth StatusMedical Order Entry SystemsAlert Fatigue, Health PersonnelDrug InteractionsFalse Positive ReactionsHumansLogistic ModelsMedical Records Systems, ComputerizedPatient Acuity

Identifiers

PMID32308847
PMCPMC7153062

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.