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Record W2769274063 · doi:10.1186/s12913-017-2750-x

The development, implementation and evaluation of clinical pathways for chronic obstructive pulmonary disease (COPD) in Saskatchewan: protocol for an interrupted times series evaluation

2017· article· en· W2769274063 on OpenAlexafffundabout
Thomas Rotter, Christopher Plishka, Mohammed Rashaad Hansia, Donna Goodridge, Erika Penz, Leigh Kinsman, Adegboyega Lawal, Sheryl O’Quinn, Nancy R. Buchan, Patricia Comfort, Prakesh Patel, Sheila Anderson, Tanya Winkel, Rae Lynn Lang, Darcy D. Marciniuk

Bibliographic record

VenueBMC Health Services Research · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsRegina Qu'Appelle Health RegionUniversity of SaskatchewanQueen's University
FundersNovartis Pharmaceuticals CanadaMinistry of Health, Saskatchewan
KeywordsMedicineOperationalizationHealth administrationContext (archaeology)Nursing researchHealth careHealth informaticsGuidelinePublic healthHealth services researchEmergency departmentClinical pathwayBaseline (sea)COPDNursingMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has substantial economic and human costs; it is expected to be the third leading cause of death worldwide by 2030. To minimize these costs high quality guidelines have been developed. However, guidelines alone rarely result in meaningful change. One method of integrating guidelines into practice is the use of clinical pathways (CPWs). CPWs bring available evidence to a range of healthcare professionals by detailing the essential steps in care and adapting guidelines to the local context. METHODS/DESIGN: We are working with local stakeholders to develop CPWs for COPD with the aims of improving care while reducing utilization. The CPWs will employ several steps including: standardizing diagnostic training, unifying components of chronic disease care, coordinating education and reconditioning programs, and ensuring care uses best practices. Further, we have worked to identify evidence-informed implementation strategies which will be tailored to the local context. We will conduct a three-year research project using an interrupted time series (ITS) design in the form of a multiple baseline approach with control groups. The CPW will be implemented in two health regions (experimental groups) and two health regions will act as controls (control groups). The experimental and control groups will each contain an urban and rural health region. Primary outcomes for the study will be quality of care operationalized using hospital readmission rates and emergency department (ED) presentation rates. Secondary outcomes will be healthcare utilization and guideline adherence, operationalized using hospital admission rates, hospital length of stay and general practitioner (GP) visits. Results will be analyzed using segmented regression analysis. DISCUSSION: Funding has been procured from multiple stakeholders. The project has been deemed exempt from ethics review as it is a quality improvement project. Intervention implementation is expected to begin in summer of 2017. This project is expected to improve quality of care and reduce healthcare utilization. In addition it will provide evidence on the effects of CPWs in both urban and rural settings. If the CPWs are found effective we will work with all stakeholders to implement similar CPWs in surrounding health regions. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT03075709 ). Registered 8 March 2017.

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)0.137
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.989
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1370.113
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0050.008
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0040.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0470.007

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 head0.645
GPT teacher head0.689
Teacher spread0.043 · 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 designNon-randomized 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

Citations54
Published2017
Admission routes3
Has abstractyes

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