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Record W2075303271 · doi:10.12927/hcq.2014.23880

"The Largest Lean Transformation in the World": The Implementation and Evaluation of Lean in Saskatchewan Healthcare

2014· article· en· W2075303271 on OpenAlexaffabout
Leigh Kinsman, Thomas Rotter, Katherine Stevenson, Brenna Bath, Donna Goodridge, Elizabeth Harrison, Nazmi Sari, Cathy Jeffery, Carrie Bourassa

Bibliographic record

VenueHealthcare Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsFirst Nations University of CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsHealth careLean manufacturingBusinessScope (computer science)Process managementQuality managementScale (ratio)Operations managementKnowledge managementMarketingComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The Saskatchewan Ministry of Health has committed to a multi-million dollar investment toward the implementation of Lean methodology across the province's healthcare system. Originating as a production line discipline (the Toyota Production System), Lean has evolved to encompass process improvements including inventory management, waste reduction and quality improvement techniques. With an initial focus on leadership, strategic alignment, training and the creation of a supportive infrastructure (Lean promotion offices), the goal in Saskatchewan is a whole health system transformation that produces "better health, better value, better care, and better teams." Given the scope and scale of the initiative and the commitment of resources, it is vital that a comprehensive, longitudinal evaluation plan be implemented to support ongoing decision-making and program design. The nature of the initiative also offers a unique opportunity to contribute to health quality improvement science by advancing our understanding of the implementation and evaluation of complex, large-scale healthcare interventions. The purpose of this article is to summarize the background to Lean in Saskatchewan and the proposed evaluation methods.

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 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)0.026
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.089
GPT teacher head0.480
Teacher spread0.391 · 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 teacher head, not a consensus.

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

Citations34
Published2014
Admission routes2
Has abstractyes

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