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Record W2042857517 · doi:10.12927/hcq.2009.20978

Improving Communication of Critical Test Results in a Pediatric Academic Setting: Key Lessons in Achieving and Sustaining Positive Outcomes

2009· article· en· W2042857517 on OpenAlexaffabout
Cheryl Jackson, Maureen J. MacDonald, Michael Anderson, Polly Stevens, Philip Gordon, Ronald M. Laxer

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsTest (biology)Best practiceKey (lock)MedicineMedical educationProcess managementNursingPsychologyBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

By applying the Institute for Healthcare Improvement's framework for strategic change (will, ideas and execution), The Hospital for Sick Children, in Toronto, Ontario, developed processes to improve patient safety through the effective communication of critical test results. In response to an adverse patient event, near misses and accreditation requirements, a task force with representatives from the laboratories and clinical services was established to ensure the timely and reliable communication of critical test results for biochemistry, hematology, coagulation, therapeutic drug monitoring and microbiology. The task force critically assessed processes and best practices, identified practical alternatives, tested changes, codified new processes in a hospital-wide policy and procedure and carried out post-implementation outcome audits. Lessons learned in sustaining improvements included the following: there is value in identifying strategies from a larger system perspective; there exist merits to working collaboratively as an inter-professional team (i.e., laboratory and clinical leaders); there is value in learning from failure; higher-cost but "higher-leverage" approaches can be pivotal; and regular monitoring and vigilance of policy compliance are required.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.398
Teacher spread0.375 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical · Commentary

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

Citations10
Published2009
Admission routes2
Has abstractyes

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