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Record W4283794990 · doi:10.5430/jha.v11n1p35

Impact of a Teletriage program on left without being seen rates and cost

2022· article· en· W4283794990 on OpenAlexvenueno aff
Andrea Blome, Stephanie Anderson, Mandy Middlebrook-Lovett, Jon Michael Cuba, Jeffrey Kuo, Nicholas Gorham, Lauren Defrates, Nicole McCoin

Bibliographic record

VenueJournal of Hospital Administration · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingOperations managementMedicineConsolidation (business)Resource useCost analysisThroughputWaiting listHealth careMedical emergencyBusinessComputer scienceNursingSurgeryOperations researchEngineeringEnvironmental resource managementOperating system

Abstract

fetched live from OpenAlex

Objective: Emergency Departments (EDs) experience throughput constraints for various reasons, such as space, resources, staffing, and bed placement. These throughput constraints are known to increase the volume of patients who leave without being evaluated. TeleTriage is a method implemented shortly after the arrival of the patient to the ED, as a means to expedite evaluation of patients. The project aimed to implement a TeleTriage program and analyze any impact on Left Without Being Seen (LWBS) rates and cost.Methods: A TeleTriage program was developed within a large, nonprofit, academic health care delivery system. The program was piloted at several campuses and subsequently implemented at multiple sites within the health system. Data on LWBS rates were collected for patients evaluated by the TeleTriage process and those who were not. An analysis of staffing utilization and cost-savings was also performed.Results: The TeleTriage program resulted in an average LWBS rate of 0.12% post-implementation, versus 0.79% for patients who were not in the TeleTriage group. In addition, the staffing consolidation resulted in cost-savings.Conclusions: The use of a TeleTriage program results in decreased LWBS rates, as well as cost-savings.

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)0.000
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 score0.034
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.464
Teacher spread0.417 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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