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Record W2110645067 · doi:10.1186/1748-5908-8-55

Best strategies to implement clinical pathways in an emergency department setting: study protocol for a cluster randomized controlled trial

2013· article· en· W2110645067 on OpenAlexafffund
Mona Jabbour, Janet Curran, Shannon D. Scott, Astrid Guttman, Thomas Rotter, Francine M. Ducharme, M. Diane Lougheed, M Louise McNaughton-Filion, Amanda S. Newton, M. Sharon Shafir, P. Alison Paprica, Terry P. Klassen, Monica Taljaard, Jeremy Grimshaw, David W. Johnson

Bibliographic record

VenueImplementation Science · 2013
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of CalgaryUniversity of ManitobaChildren's Hospital Research Institute of ManitobaMcMaster UniversityCambridge Memorial HospitalChamplain Regional CollegeOttawa HospitalMontfort HospitalQueen's UniversityDalhousie UniversityCentre Hospitalier Universitaire Sainte-JustineUniversity of SaskatchewanUniversité de MontréalHospital for Sick ChildrenUniversity of TorontoUniversity of AlbertaAlberta Children's HospitalIzaak Walton Killam Health CentreUniversity of OttawaInstitute for Clinical Evaluative SciencesMinistry of Health and Long Term CareChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health ResearchIWK Health CentreAlberta Heritage Foundation for Medical Research
KeywordsMedicineClinical pathwayEmergency departmentAuditRandomized controlled trialHealth administrationHealth services researchHealth informaticsIntervention (counseling)Protocol (science)Clinical trialCluster randomised controlled trialMedical emergencyPublic healthNursingAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The clinical pathway is a tool that operationalizes best evidence recommendations and clinical practice guidelines in an accessible format for 'point of care' management by multidisciplinary health teams in hospital settings. While high-quality, expert-developed clinical pathways have many potential benefits, their impact has been limited by variable implementation strategies and suboptimal research designs. Best strategies for implementing pathways into hospital settings remain unknown. This study will seek to develop and comprehensively evaluate best strategies for effective local implementation of externally developed expert clinical pathways. DESIGN/METHODS: We will develop a theory-based and knowledge user-informed intervention strategy to implement two pediatric clinical pathways: asthma and gastroenteritis. Using a balanced incomplete block design, we will randomize 16 community emergency departments to receive the intervention for one clinical pathway and serve as control for the alternate clinical pathway, thus conducting two cluster randomized controlled trials to evaluate this implementation intervention. A minimization procedure will be used to randomize sites. Intervention sites will receive a tailored strategy to support full clinical pathway implementation. We will evaluate implementation strategy effectiveness through measurement of relevant process and clinical outcomes. The primary process outcome will be the presence of an appropriately completed clinical pathway on the chart for relevant patients. Primary clinical outcomes for each clinical pathway include the following: Asthma--the proportion of asthmatic patients treated appropriately with corticosteroids in the emergency department and at discharge; and Gastroenteritis--the proportion of relevant patients appropriately treated with oral rehydration therapy. Data sources include chart audits, administrative databases, environmental scans, and qualitative interviews. We will also conduct an overall process evaluation to assess the implementation strategy and an economic analysis to evaluate implementation costs and benefits. DISCUSSION: This study will contribute to the body of evidence supporting effective strategies for clinical pathway implementation, and ultimately reducing the research to practice gaps by operationalizing best evidence care recommendations through effective use of clinical pathways. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01815710.

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)0.064
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.087
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.074
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0140.009
Bibliometrics0.0040.005
Science and technology studies0.0040.005
Scholarly communication0.0070.005
Open science0.0040.003
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0870.011

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 head0.414
GPT teacher head0.650
Teacher spread0.235 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations63
Published2013
Admission routes2
Has abstractyes

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