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Record W4386316434 · doi:10.1097/qmh.0000000000000427

Reduction of Chest Drain Overuse Through Implementation of a Pleural Drainage Order Set

2023· article· en· W4386316434 on OpenAlexaff
Pattraporn Tajarernmuang, David Valenti, Anne V. Gonzalez, Giovanni Artho, Mary Tsatoumas, Stéphane Beaudoin

Bibliographic record

VenueQuality Management in Health Care · 2023
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineThoracentesisDrainageSurgeryPleural diseasePleural effusionRespiratory diseaseInternal medicineLung

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Small chest drains are used in many centers as the default drainage strategy for various pleural effusions. This can lead to drain overuse, which may be harmful. This study aimed to reduce chest drain overuse. METHODS: We studied consecutive pleural procedures performed in the radiology department before (August 1, 2015, to July 31, 2016) and after intervention (September 1, 2019, to January 31, 2020). Chest drains were deemed indicated or not based on criteria established by a local interdisciplinary work group. The intervention consisted of a pleural drainage order set embedded in electronic medical records. It included indications for chest drain insertion, prespecified drain sizes for each indication, fluid analyses, and postprocedure radiography orders. Overall chest drain use and proportion of nonindicated drains were the outcomes of interest. RESULTS: We reviewed a total of 288 procedures (pre-intervention) and 155 procedures (post-intervention) (thoracentesis and drains). Order-set implementation led to a reduction in drain use (86.5% vs 54.8% of all procedures, P < .001) and reduction in drain insertions in the absence of an indication (from 45.4% to 29.4% of drains, P = .01). The need for repeat procedures did not increase after order-set implementation (22.0% pre vs 17.7% post, P = .40). Complication rates and length of hospital stay did not differ significantly after the intervention. More pleural infections were treated with drain sizes of 12Fr and greater (31 vs 70%, P < .001) after order-set deployment, and direct procedural costs were reduced by 27 CAN$ per procedure. CONCLUSION: Implementation of a pleural drainage order-set reduced chest drain use, improved procedure selection according to clinical needs, and reduced direct procedural costs. In institutions where small chest drains are used as the default drainage strategy for pleural effusions, this order set can reduce chest drain overuse.

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)0.003
metaresearch head score (Gemma)0.020
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.093
GPT teacher head0.450
Teacher spread0.356 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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