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Record W2913326501 · doi:10.12927/hcq.2018.25706

Clinically Integrated Supply Chain Infrastructure in Health Systems: The Opportunity to Improve Quality and Safety

2018· article· en· W2913326501 on OpenAlexvenueaboutno aff
Anne Snowdon

Bibliographic record

VenueHealthcare Quarterly · 2018
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessQuality (philosophy)Best practiceSupply chainQuality managementProcess managementPatient safetyRisk analysis (engineering)Supply chain risk managementHealthcare systemOperations managementHealth careSupply chain managementMarketingEngineeringEconomic growthService management

Abstract

fetched live from OpenAlex

Health systems worldwide are challenged by the growing costs and demands of delivering high-quality healthcare services that are safe and cost-effective to support health and wellness for populations. This paper proposes a system-level transformation of clinical environment infrastructure to advance quality and strengthen patient safety at sustainable costs. Evidence of the impact and value of the clinically integrated supply chain strategy is emerging in three global health systems: Alberta Health Services (Canada), Mercy (US) and the National Health Service (UK) to inform a strategic roadmap for health system leaders to leverage supply chain infrastructure in clinical environments as a strategic asset to strengthen quality, safety and cost.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.100
GPT teacher head0.468
Teacher spread0.368 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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