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Record W2232974535 · doi:10.2196/humanfactors.5098

Changes in Default Alarm Settings and Standard In-Service are Insufficient to Improve Alarm Fatigue in an Intensive Care Unit: A Pilot Project

2016· article· en· W2232974535 on OpenAlexvenueno aff
Azizeh Sowan, Tiffany Michelle Gomez, Albert Tarriela, Charles C. Reed, Bruce Paper

Bibliographic record

VenueJMIR Human Factors · 2016
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsALARMService (business)Intensive care unitUnit (ring theory)Medical emergencyBusinessPsychologyMedicineEngineeringIntensive care medicineMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical alarm systems safety is a national concern, specifically in intensive care units (ICUs) where alarm rates are known to be the highest. Interventional projects that examined the effect of changing default alarm settings on overall alarm rate and on clinicians' attitudes and practices toward clinical alarms and alarm fatigue are scarce. OBJECTIVE: To examine if (1) a change in default alarm settings of the cardiac monitors and (2) in-service nursing education on cardiac monitor use in an ICU would result in reducing alarm rate and in improving nurses' attitudes and practices toward clinical alarms. METHODS: This quality improvement project took place in a 20-bed transplant/cardiac ICU with a total of 39 nurses. We implemented a unit-wide change of default alarm settings involving 17 parameters of the cardiac monitors. All nurses received an in-service education on monitor use. Alarm data were collected from the audit log of the cardiac monitors 10 weeks before and 10 weeks after the change in monitors' parameters. Nurses' attitudes and practices toward clinical alarms were measured using the Healthcare Technology Foundation National Clinical Alarms Survey, pre- and postintervention. RESULTS: Alarm rate was 87.86 alarms/patient day (a total of 64,500 alarms) at the preintervention period compared to 59.18 alarms/patient day (49,319 alarms) postintervention (P=.01). At baseline, Arterial Blood Pressure (ABP), Pair Premature Ventricular Contractions (PVCs), and Peripheral Capillary Oxygen Saturation (SpO2) alarms were the highest. ABP and SpO2 alarms remained among the top three at the postproject period. Out of the 39 ICU nurses, 24 (62%) provided complete pre- and postproject survey questionnaires. Compared to the preintervention survey, no remarkable changes in the postproject period were reported in nurses' attitudes. Themes in the narrative data were related to poor usability of cardiac monitors and the frequent alarms. The data showed great variation among nurses in terms of changing alarm parameters and frequency of replacing patients' electrodes. Despite the in-service, 50% (12/24) of the nurses specified their need for more training on cardiac monitors in the postproject period. CONCLUSIONS: Changing default alarm settings and standard in-service education on cardiac monitor use are insufficient to improve alarm systems safety. Alarm management in ICUs is very complex, involving alarm management practices by clinicians, availability of unit policies and procedures, unit layout, complexity and usability of monitoring devices, and adequacy of training on system use. The complexity of the newer monitoring systems requires urgent usability testing and multidimensional interventions to improve alarm systems safety and to attain the Joint Commission National Patient Safety Goal on alarm systems safety in critical care units.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.388
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations91
Published2016
Admission routes1
Has abstractyes

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