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Record W1981172686 · doi:10.4018/ijoris.2014010106

Modeling and Simulation Analyses of Healthcare Delivery Operations for Inter-Hospital Patient Transfers

2014· article· en· W1981172686 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Operations Research and Information Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsQueen's University
Fundersnot available
KeywordsSizingHealth careOperations researchTransfer (computing)Computer scienceOperations managementQuality (philosophy)EngineeringEconomics

Abstract

fetched live from OpenAlex

Inter-hospital transfers of patients for different elements of care have been increasingly used as a common strategy for providing quality healthcares through sharing limited resources worldwide. In this paper, the authors study the problem of healthcare delivery operations for inter-hospital patient transfers motivated by a real-world case within the South East Local Health Integration Network of Ontario. The authors use a directed graph to develop a general model for obtaining the solution that minimizes the overall transportation time while satisfying all the inter-hospital transfer requests with identical or different start and end points. The authors also perform simulation analyses to study the fleet sizing problem through evaluating different service performances with different fleet sizes. A number of implementation issues for managing inter-hospital patient transfer services are also discussed.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.073
GPT teacher head0.403
Teacher spread0.329 · 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