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Record W2611847332 · doi:10.1200/jop.2016.020008

Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency

2017· article· en· W2611847332 on OpenAlex
S. Suss, Nadia Bhuiyan, Kudret Demirli, Gerald Batist

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

VenueJournal of Oncology Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsConcordia UniversityMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsReworkMedicineWork flowWork (physics)Process (computing)Queueing theoryOperations managementMedical emergencyMetropolitan areaMultidisciplinary approachProcess managementComputer scienceBusiness

Abstract

fetched live from OpenAlex

Outpatient cancer treatment centers can be considered as complex systems in which several types of medical professionals and administrative staff must coordinate their work to achieve the overall goals of providing quality patient care within budgetary constraints. In this article, we use analytical methods that have been successfully employed for other complex systems to show how a clinic can simultaneously reduce patient waiting times and non-value added staff work in a process that has a series of steps, more than one of which involves a scarce resource. The article describes the system model and the key elements in the operation that lead to staff rework and patient queuing. We propose solutions to the problems and provide a framework to evaluate clinic performance. At the time of this report, the proposals are in the process of implementation at a cancer treatment clinic in a major metropolitan hospital in Montreal, Canada.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.114
GPT teacher head0.505
Teacher spread0.391 · 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