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Record W2988541147 · doi:10.2196/14123

Types and Frequency of Infusion Pump Alarms and Infusion-Interruption to Infusion-Recovery Times for Critical Short Half-Life Infusions: Retrospective Data Analysis

2019· article· en· W2988541147 on OpenAlexvenueno aff
James Waterson, Arkadiusz Bedner

Bibliographic record

VenueJMIR Human Factors · 2019
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsInfusion pumpInfusion therapyTarget controlled infusionAnesthesiaMedicineContinuous infusion

Abstract

fetched live from OpenAlex

BACKGROUND: Alarm fatigue commonly leads to a reduced response to alarms. Appropriate and timely response to intravenous pump alarms is crucial to infusion continuity. The difficulty of filtering out critical short half-life infusion alarms from nonurgent alarms is a key challenge for risk management for clinicians. Critical care areas provide ample opportunities for intravenous medication error with the frequent administration of high-alert, critical short half-life infusions that require rigorous maintenance for continuity of delivery. Most serious medication errors in critical care occur during the execution of treatment, with performance-level failures outweighing rule-based or knowledge-based mistakes. OBJECTIVE: One objective of this study was to establish baseline data for the types and frequency of alarms that critical care clinicians are exposed to from a variety of infusion devices, including both large volume pumps and syringe drivers. Another objective was to identify the volume of these alarms that specifically relate to critical short half-life infusions and to evaluate user response times to alarms from infusion devices delivering these particular infusions. METHODS: The event logs of 1183 infusion pumps used in critical care environments and in general care areas within the European region were mined for a range of alarm states. The study then focused on a selection of infusion alarms from devices delivering critical short half-life infusions that would warrant rapid attention from clinicians in order to avoid potentially harmful prolonged infusion interruption. The reaction time of clinicians to infusion-interruption states and alarms for the selected critical short half-life infusions was then calculated. RESULTS: Initial analysis showed a mean average of 4.50 alarms per infusion in the general critical care pump population as opposed to the whole hospital rate of 1.39. In the pediatric intensive care unit (PICU) group, the alarms per infusion value was significantly above the mean average for all critical care areas, with 8.61 alarms per infusion. Infusion-interruption of critical short half-life infusions was found to be a significant problem in all areas of the general critical care pump population, with a significant number of downstream (ie, vein and access) occlusion events noted. While the mean and median response times to critical short half-life infusion interruptions were generally within the half-lives of the selected medications, there was a high prevalence of outliers in terms of reaction times for all the critical short half-life infusions studied. CONCLUSIONS: This study gives an indication of what might be expected in critical care environments in terms of the volume of general infusion alarms and critical short half-life infusion alarms, as well as for clinician reaction times to critical short half-life infusion-interruption events. This study also identifies potentially problematic areas of the hospital for alarm fatigue and for particular issues of infusion and infusion-line management. Application of the proposed protocols can help create benchmarks for pump alarm management and clinician reaction times. These protocols can be applied to studies on the impact of alarm fatigue and for the evaluation of protocols, infusion-monitoring strategies, and infusion pump-based medication safety software aimed at reducing alarm fatigue and ensuring the maintenance of critical short half-life infusions. Given the frequency of infusion alarms seen in this study, the risk of alarm fatigue due to the white noise of pump alarms present in critical care, to which clinicians are constantly exposed, is very high. Furthermore, the added difficulties of maintaining critical short half-life infusions, and other infusions in specialist areas, are made clear by the high ratio of downstream occlusion to infusion starts in the neonatal intensive care unit (NICU). The ability to quantitatively track the volume of alarms and clinician reaction times contributes to a greater understanding of the issues of alarm fatigue in intensive care units. This can be applied to clinical audit, can allow for targeted training to reduce nuisance alarms, and can aid in planning for improvement in the key area of maintenance of steady-state plasma levels of critical short half-life infusions. One clear conclusion is that the medication administration rights should be extended to include right maintenance and ensured delivery continuity of critical short half-life infusions.

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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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.071
GPT teacher head0.394
Teacher spread0.323 · 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 designObservational
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".

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Citations27
Published2019
Admission routes1
Has abstractyes

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