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Record W4313020197 · doi:10.1016/j.ifacol.2022.09.414

Patient Scheduling: The Case of an Iranian Cardiology Clinic

2022· article· en· W4313020197 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.

Bibliographic record

VenueIFAC-PapersOnLine · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineScheduleTime constraintScheduling (production processes)Constraint programmingConstraint (computer-aided design)Emergency medicineMedical emergencyFamily medicineComputer scienceOperations managementEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Across the world, cardiovascular diseases (CVD) are among the leading causes of death. In Iran, it is estimated that about 46% of all the reported deaths is related to CVD. This article focuses on the patient scheduling practices of a private cardiology clinic in Iran. Several complaints from the patients and staff members of the clinic are reviewed. The study shows that the patients in the clinic are classified into six major groups; the steps each group must undergo in the clinic as well as the time related to each operation is measured. A constraint programming model is developed to schedule the patients and rectify the complaints. Computational results based on 30 days of actual data from the clinic reveals that the proposed model manages to significantly improve the efficiency measures and is successful in resolving the causes of complaints. Furthermore, the developed constraint programming generates optimum solutions in a rather short amount of time.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.394
Teacher spread0.279 · 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