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Record W1983972904 · doi:10.1309/ajcp3yxid2uhzpht

The Effect of a Lean Quality Improvement Implementation Program on Surgical Pathology Specimen Accessioning and Gross Preparation Error Frequency

2012· article· en· W1983972904 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandUniversity of Washington
KeywordsLean manufacturingRoot cause analysisKaizenQuality managementMedicinePatient safetyOperations managementProcess (computing)Work (physics)Computer sciencePathologyReliability engineeringManagement systemEngineeringHealth care

Abstract

fetched live from OpenAlex

Few reports have documented the effectiveness of Lean quality improvement in changing anatomic pathology patient safety. We used Lean methods of education; hoshin kanri goal setting and culture change; kaizen events; observation of work activities, hand-offs, and pathways; A3-problem solving, metric development, and measurement; and frontline work redesign in the accessioning and gross examination areas of an anatomic pathology laboratory. We compared the pre- and post-Lean implementation proportion of near-miss events and changes made in specific work processes. In the implementation phase, we documented 29 individual A3-root cause analyses. The pre- and postimplementation proportions of process- and operator-dependent near-miss events were 5.5 and 1.8 (P < .002) and 0.6 and 0.6, respectively. We conclude that through culture change and implementation of specific work process changes, Lean implementation may improve pathology patient safety.

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)0.024
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.185
GPT teacher head0.647
Teacher spread0.463 · 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