MétaCan
Menu
Back to cohort
Record W2989895136 · doi:10.1097/dcc.0000000000000397

Bridging the Communication Gap

2019· article· en· W2989895136 on OpenAlexaff
Andy Griffith, Stacy Haverstick, Deb Blissick, Teresa Colaianne, Heidi Shields, Caty Johnson, Rená Lucier, Mary Jane Melong, Kristin Kasten, Kevin Knott

Bibliographic record

VenueDimensions of Critical Care Nursing · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsBridging (networking)Computer scienceComputer network

Abstract

fetched live from OpenAlex

BACKGROUND: As of December 31, 2016, in the United States, 22 866 patients received left ventricular assist devices (LVADs) (J Heart Lung Transplant. 2017;36(10):1080-1086). First responders are generally unfamiliar with LVAD equipment functionality (J Heart Lung Transplant. 2018;37(4):S275). When a patient has an emergency either clinically or with a controller alarm or failure, speaking with ventricle assist device (VAD)-trained personnel is imperative to the prevention of adverse events. Starting February 2017, an LVAD program totaling 181 patients at a large teaching hospital changed their afterhours process to reduce wait time between patient call and talking to VAD-trained personnel to increase patient safety and patient satisfaction. METHODS: The Plan-Do-Check-Act quality improvement method was used to evaluate this project from February 2017 to July 2018 by the program's clinical information analyst. An afterhours summary of telephone interactions between VAD program clinicians (VAD coordinators, physician assistants, and nurse practitioner) was used to analyze the use of the "VAD Emergency Line." An annual patient satisfaction survey was completed to analyze patient satisfaction of the VAD Emergency Line. INTERVENTIONS: Review of the afterhours summary was conducted to determine the use of the VAD Emergency Line. The process of afterhours patient calls was changed so that calls are answered immediately by a 24-hour LVAD-trained medical ambulance service, called VAD Emergency Line. Patient use of the VAD Emergency Line was continuously assessed. In November 2017, it was recognized that only 57% of patient calls used the VAD Emergency Line, and further intervention was needed. In November 2017, patients were provided visual reminders to ensure compliance. RESULTS: Seventeen months after the implementation of the VAD Emergency Line, 92% of patient's afterhours calls were through the VAD Emergency Line. Although there was no statistical significance found, there was clinical significance. Since the implementation of the VAD Emergency Line, patient use of the VAD Emergency Line increased 56% from March 2017 to July 2018. There have been zero adverse safety events. Sixty-one percent of patients strongly agreed to the question "You are able to communicate emergent needs after hours (VAD Emergency Line)? CONCLUSION: Implementation of the LVAD Emergency Line has improved communication between patients in the outpatient setting. This increased patient safety by allowing patients to speak to LVAD-trained first responders and VAD coordinator personnel immediately without ever being put on hold. This communication process can be applied to other clinical programs.

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)0.019
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0140.021
Open science0.0030.019
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0520.012

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 designQualitative
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDimensions of Critical Care NursingSame topicMechanical Circulatory Support DevicesFrench-language works237,207