Evidence map›Paper›PMID 26499101›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts.

Robert B McDaniel, Jonathan D Burlison, Donald K Baker, Murad Hasan, Jennifer Robertson, Christine Hartford, Scott C Howard, Andras Sablauer, James M Hoffman

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. A Discount Approach to Reducing Nursing Alert Burden.Applied clinical informatics · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Seeking the Right Time for Venous Thromboembolism Prophylaxis Alerts.The Brown journal of hospital medicine · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Testing modes of computerized sepsis alert notification delivery systems.BMC medical informatics and decision making · 2016
    Article
  17. Integrating pharmacogenomics into electronic health records with clinical decision support.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2016
    Review
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

9 authors.

Robert B McDanielDepartment of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Jonathan D BurlisonDepartment of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Donald K BakerDepartment of Information Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Murad HasanDepartment of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Jennifer RobertsonDepartment of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Christine HartfordDepartment of Bone Marrow Transplantation and Cellular Therapy, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
Scott C HowardDepartment of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee, USA, University of Memphis, Memphis, Tennessee, USA.
Andras SablauerDepartment of Information Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
James M HoffmanDepartment of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, Tennessee, USA Department of Clinical Pharmacy, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee, USA James.Hoffman@stjude.org.

Funding

Viral Vector Technology (VVTSR)P30CA021765 · NCI · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI CHARLES ROBERTS · 1985 to 2026
$166.9M
NCI NIH HHS NCI CA 21765
6 · The paper itself

Abstract

Metrics for evaluating interruptive prescribing alerts have many limitations. Additional methods are needed to identify opportunities to improve alerting systems and prevent alert fatigue. In this study, the authors determined whether alert dwell time-the time elapsed from when an interruptive alert is generated to when it is dismissed-could be calculated by using historical alert data from log files. Drug-drug interaction (DDI) alerts from 3 years of electronic health record data were queried. Alert dwell time was calculated for 25,965 alerts, including 777 unique DDIs. The median alert dwell time was 8 s (range, 1-4913 s). Resident physicians had longer median alert dwell times than other prescribers (P < 001). The 10 most frequent DDI alerts (n = 8759 alerts) had shorter median dwell times than alerts that only occurred once (P < 001). This metric can be used in future research to evaluate the effectiveness and efficiency of interruptive prescribing alerts.

Indexed as

Clinical AlarmsDecision Support Systems, ClinicalMedical Order Entry SystemsElectronic Health RecordsHumansReminder SystemsTime Factorsclinical decision supportcomputerized prescriber order entryelectronic health recordinterruptive alertsoverride rates

Identifiers

PMID26499101
PMCPMC4954613

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.