Evidence map›Paper›PMID 33339028›Full record

Observational studyBiomedical instrumentation & technology2020

Analysis: Protocol for a New Method to Measure Physiologic Monitor Alarm Responsiveness.

Brooke Luo, Melissa McLoone, Irit R Rasooly, Sansanee Craig, Naveen Muthu, James Won, Halley Ruppel, Christopher P Bonafide

Open access · bronzeAbstract readObservational Study
In one paragraph

Observational study in Biomedical instrumentation & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 3 institutions in 1 country.

Brooke Luo
Melissa McLoone
Irit R Rasooly
Sansanee Craig
Naveen Muthu
James Won
Halley Ruppel
Christopher P Bonafide
University of Pennsylvania · USChildren's Hospital of Philadelphia · USKaiser Permanente · US

Funding

Pediatric patient safety learning laboratory to re-engineer continuous physiologic monitoring systemsR18HS026620 · AHRQ · CHILDREN'S HOSP OF PHILADELPHIA · PI BONAFIDE, CHRISTOPHER PETER · 2018 to 2021
$2.4M
AHRQ HHS R18 HS026620
6 · The paper itself

Abstract

Evaluating the clinical impacts of healthcare alarm management systems plays a critical role in assessing newly implemented monitoring technology, exposing latent threats to patient safety, and identifying opportunities for system improvement. We describe a novel, accurate, rapidly implementable, and readily reproducible in situ simulation approach to measure alarm response times and rates without the challenges and expense of video analysis. An interprofessional team consisting of biomedical engineers, human factors engineers, information technology specialists, nurses, physicians, facilitators from the hospital's simulation center, clinical informaticians, and hospital administrative leadership worked with three units at a pediatric hospital to design and conduct the simulations. Existing hospital technology was used to transmit a simulated, unambiguously critical alarm that appeared to originate from an actual patient to the nurse's mobile device, and discreet observers measured responses. Simulation observational data can be used to design and evaluate quality improvement efforts to address alarm responsiveness and to benchmark performance of different alarm communication systems.

Indexed as

Clinical AlarmsChildHospitals, PediatricHumansMonitoring, PhysiologicQuality Improvement

Identifiers

PMID33339028
PMCPMC7769130
OpenAlexW3115019301

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.