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Record W2110387449 · doi:10.12927/hcq.2013.19855

Creating Sustained Improvements in Patient Access and Flow: Experiences from Three Ontario Healthcare Institutions

2008· article· en· W2110387449 on OpenAlexaffabout
Hugh MacLeod, Bob Bell, Ken Deane, Carolyn Baker

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsMinistry of Health and Long Term Care
Fundersnot available
KeywordsGeneral partnershipHealth careNursingMedicineQuality managementTransformational leadershipMedical emergencyBusinessPublic relationsOperations managementPolitical scienceManagement system

Abstract

fetched live from OpenAlex

Ensuring that patients receive timely, high-quality healthcare is the highest priority of Ontario's hospitals, physicians and nurses. Given that the emergency department (ED) is often the "front door" to our healthcare system, developing approaches to improve access and flow in the ED is important - made more challenging by rising patient demand and acuity. Long-standing efforts to improve the ED system have outlined promising approaches and pushed access and flow up the priority list. Recently, in partnership with the Ministry of Health and Long-Term Care (MOHLTC), several Ontario hospitals participated in an intensive and sustained effort to improve access and flow, with promising results. Participants in these efforts described the initiatives as transformational, and the results have been promising and sustained. This article chronicles the efforts of three hospitals to enable other hospitals, physicians and nurses to learn from these experiences and gain confidence that a similar impact can be achieved in their facilities. Specifically, it discusses the following: The three pillars of sustainable transformation. Hospital case studies. St. Joseph's Health Centre (SJHC), Toronto. London Health Sciences Centre (LHSC) - University Hospital. University Health Network (UHN) - Toronto General and Toronto Western. Advice for other hospitals

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.339
Teacher spread0.290 · 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 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

Citations9
Published2008
Admission routes2
Has abstractyes

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