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Record W2072129191 · doi:10.1109/ccece.2010.5575216

Analysis of time cost for alternatives to enhance efficiency within the medical emergency referral system in Alberta

2010· article· en· W2072129191 on OpenAlexaffabout
Simon Ferrari, J. P. H. Wyse, Yaoping Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsInefficiencyReferralInterfacingProcess (computing)Computer scienceMedical emergencyOperations managementProcess managementBusinessMedicineNursingEngineeringOperating system

Abstract

fetched live from OpenAlex

As the first step of an endeavor to remedy the issue of administrative inefficiency in the emergency referral system of Alberta Health Services, we present a study which analyzes the administrative time cost (excluding patient assessment time) of two alternative processes compared to the current process of referral. One alternative is a label/standardized form process which uses our emergency consultation chit to standardize referral information. The other, an electronic process, utilizes a software prototype we developed on two networked computers using Java and open source libraries. The electronic process is capable of streamlining referral information transfer between organizations in the emergency referral system and interfacing with existing Electronic Medical Record systems. The study results reveal that both alternative processes reduce administrative time cost compared to the current referral process. The electronic process demonstrates a technically feasible and time-cost effective remedy for the issue of administrative inefficiency.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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 designTheoretical or conceptual
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

Citations0
Published2010
Admission routes2
Has abstractyes

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