Evidence map›Paper›PMID 29768477›Full record

Trial reportPloS one2018

Faster clinical response to the onset of adverse events: A wearable metacognitive attention aid for nurse triage of clinical alarms.

Daniel C McFarlane, Alexa K Doig, James A Agutter, Lara M Brewer, Noah D Syroid, Ranjeev Mittu

Abstract readClinical Trial
In one paragraph

Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Innovative Technologies: Optimizing Nursing Practice.Computers, informatics, nursing : CIN · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Observational
  7. Article
  8. Acoustic Biotopes, Listeners and Sound-Induced Action: A Case Study of Operating Rooms.International journal of environmental research and public health · 2022
    Article
  9. Review
  10. Article
  11. Article
  12. Organizing Audible Alarm Sounds in the Hospital: A Card-Sorting Study.IEEE transactions on human-machine systems · 2020
    Article
  13. Article
  14. Article
  15. 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.

Daniel C McFarlanePatient Care & Monitoring Solutions, Innovation Office, Royal Philips, Andover, Massachusetts, United States of America.ORCID 0000-0002-0561-2634
Alexa K DoigCollege of Nursing, University of Utah, Salt Lake City, Utah, United States of America.
James A AgutterCollege of Architecture & Planning, University of Utah, Salt Lake City, Utah, United States of America.
Lara M BrewerDepartment of Anesthesiology, University of Utah Health Sciences Center, Salt Lake City, Utah, United States of America.
Noah D SyroidApplied Medical Visualizations (Medvis), Salt Lake City, Utah, United States of America.
Ranjeev MittuInformation Technology Division, Information Management and Decision Architectures Branch, United States Naval Research Laboratory, Washington, District of Columbia, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study evaluates the potential for improving patient safety by introducing a metacognitive attention aid that enables clinicians to more easily access and use existing alarm/alert information. It is hypothesized that this introduction will enable clinicians to easily triage alarm/alert events and quickly recognize emergent opportunities to adapt care delivery. The resulting faster response to clinically important alarms/alerts has the potential to prevent adverse events and reduce healthcare costs. MATERIALS AND

methodsA randomized within-subjects single-factor clinical experiment was conducted in a high-fidelity 20-bed simulated acute care hospital unit. Sixteen registered nurses, four at a time, cared for five simulated patients each. A two-part highly realistic clinical scenario was used that included representative: tasking; information; and alarms/alerts. The treatment condition introduced an integrated wearable attention aid that leveraged metacognition methods from proven military systems. The primary metric was time for nurses to respond to important alarms/alerts.

resultsUse of the wearable attention aid resulted in a median relative within-subject improvement for individual nurses of 118% (W = 183, p = 0.006). The top quarter of relative improvement was 3,303% faster (mean; 17.76 minutes reduced to 1.33). For all unit sessions, there was an overall 148% median faster response time to important alarms (8.12 minutes reduced to 3.27; U = 2.401, p = 0.016), with 153% median improvement in consistency across nurses (F = 11.670, p = 0.001). DISCUSSION AND

conclusionExisting device-centric alarm/alert notification solutions can require too much time and effort for nurses to access and understand. As a result, nurses may ignore alarms/alerts as they focus on other important work. There has been extensive research on reducing alarm frequency in healthcare. However, alarm safety remains a top problem. Empirical observations reported here highlight the potential of improving patient safety by supporting the meta-work of checking alarms.

Indexed as

AttentionMetacognitionNursesTriageClinical AlarmsFemaleHumansMaleWearable Electronic Devices

Identifiers

PMID29768477
PMCPMC5955574

What OpenQuestion holds

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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.