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Record W2334242164 · doi:10.1097/qmh.0b013e3182033791

A Healthcare Lean Six Sigma System for Postanesthesia Care Unit Workflow Improvement

2011· article· en· W2334242164 on OpenAlex
Alex Kuo, Elizabeth M. Borycki, André Kushniruk, Te-Shu Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality Management in Health Care · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLean Six SigmaSix SigmaWorkflowHealth careHuman performance technologyProcess managementDesign for Six SigmaUnit (ring theory)Quality managementLean project managementHealthcare serviceLean manufacturingComputer scienceBusinessOperations managementService (business)EngineeringPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this article is to propose a new model called Healthcare Lean Six Sigma System that integrates Lean and Six Sigma methodologies to improve workflow in a postanesthesia care unit. METHODS: The methodology of the proposed model is fully described. A postanesthesia care unit case study is also used to demonstrate the benefits of using the Healthcare Lean Six Sigma System model by combining Lean and Six Sigma methodologies together. RESULTS: The new model bridges the service gaps between health care providers and patients, balances the requirements of health care managers, and delivers health care services to patients by taking the benefits of the Lean speed and Six Sigma high-quality principles. CONCLUSIONS: The full benefits of the new model will be realized when applied at both strategic and operational levels. For further research, we will examine how the proposed model is used in different real-world case studies.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.079
GPT teacher head0.320
Teacher spread0.242 · 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