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Record W2989895136 · doi:10.1097/dcc.0000000000000397

Bridging the Communication Gap

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
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