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Record W2745006824 · doi:10.1186/s12913-017-2477-8

An economic analysis of a system wide Lean approach: cost estimations for the implementation of Lean in the Saskatchewan healthcare system for 2012–2014

2017· article· en· W2745006824 on OpenAlexaffabout
Nazmi Sari, Thomas Rotter, Donna Goodridge, Elizabeth Harrison, Leigh Kinsman

Bibliographic record

VenueBMC Health Services Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsKaizenHealth administrationOperations managementHealth careLean manufacturingPromotion (chess)AccountabilityBusinessQuality costsNursing researchTotal costMedicineAccountingEconomicsNursingEconomic growthCost control

Abstract

fetched live from OpenAlex

BACKGROUND: The costs of investing in health care reform initiatives to improve quality and safety have been underreported and are often underestimated. This paper reports direct and indirect cost estimates for the initial phase of the province-wide implementation of Lean activities in Saskatchewan, Canada. METHODS: In order to obtain detailed information about each type of Lean event, as well as the total number of corresponding Lean events, we used the Provincial Kaizen Promotion Office (PKPO) Kaizen database. While the indirect cost of Lean implementation has been estimated using the corresponding wage rate for the event participants, the direct cost has been estimated using the fees paid to the consultant and other relevant expenses. RESULTS: The total cost for implementation of Lean over two years (2012-2014), including consultants and new hires, ranged from $44 million CAD to $49.6 million CAD, depending upon the assumptions used. Consultant costs accounted for close to 50% of the total. The estimated cost of Lean events alone ranged from $16 million CAD to $19.5 million CAD, with Rapid Process Improvement Workshops requiring the highest input of resources. CONCLUSIONS: Recognizing the substantial financial and human investments required to undertake reforms designed to improve quality and contain cost, policy makers must carefully consider whether and how these efforts result in the desired transformations. Evaluation of the outcomes of these investments must be part of the accountability framework, even prior to implementation.

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)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.121
GPT teacher head0.446
Teacher spread0.326 · 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.

Study designObservational
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

Citations21
Published2017
Admission routes2
Has abstractyes

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