Evidence map›Paper›PMID 33777543›Full record

ArticleIEEE transactions on human-machine systems2020

Organizing Audible Alarm Sounds in the Hospital: A Card-Sorting Study.

Melanie C Wright, Sydney Radcliffe, Suzanne Janzen, Judy Edworthy, Thomas Reese, Noa Segall

Abstract read
In one paragraph

Article in IEEE transactions on human-machine systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Melanie C WrightIdaho State University, Meridian, ID 83642 USA.
Sydney RadcliffeSaint Alphonsus Regional Medical Center, Boise, ID 83706 USA.
Suzanne JanzenSaint Alphonsus Regional Medical Center, Boise, ID 83706 USA.
Judy EdworthyUniversity of Plymouth, Plymouth UK. Dr. Edworthy provides consulting to Masimo.
Thomas ReeseUniversity of Utah, Salt Lake City, UT USA.
Noa SegallDuke University, Durham, NC USA.

Funding

UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

In hospitals, clinicians are presented with varied and disorganized alarm sounds from disparate devices. While there has been attention to reducing inactionable alarms to address alarm overload, little effort has focused on organizing, simplifying, or improving the informativeness of alarms. We sought to elicit nurses' tacit interpretation of alarm events to create an organizational structure to inform the design of advanced alarm sounds or integrated alert systems. We used open card sorting to evaluate nurses' perception of the relatedness of different alarm events. Seventy hospital nurses sorted 89 alarm events into groups they believed could or should be indicated by the same sound. We conducted factor analysis on a similarity matrix of frequency of alarm event pairings to interpret how strongly alarm events loaded on different alarm groups (factors). We interpreted participants' grouping rationale from their group labels and comments. Urgency of response was the most common grouping rationale. Participants also grouped: 1) monitoring-related events, 2) device-related events, and 3) events related to calls and patients. Our findings support standardization and integration of alarm sounds across devices toward a simpler and more informative hospital alarm environment.

Indexed as

Alarm designauditory displayshealthcare safetyhuman factorsinterruptionknowledge elicitation

Identifiers

PMID33777543
PMCPMC7996481

What OpenQuestion holds

Textmetadata
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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.