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Record W3193727037 · doi:10.1177/08404704211038215

Modern communications technology: An essential tool for optimizing hospital operations and improving outcomes

2021· article· en· W3193727037 on OpenAlexaff
Benjamin R. Kanter

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsKey (lock)Component (thermodynamics)Computer scienceWork (physics)Action (physics)Process managementKnowledge managementEmbodied cognitionOperations managementBusinessComputer securityEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

An ability to rapidly convert data from multiple different sources into actionable information is embodied in a concept called Real-time Health Systems (RTHS). The foundational component of RTHS is a modern Clinical Communication and Collaboration (CC&C) Platform, which translates organizational knowledge into action. Effective communication is the key. A CC&C Platform that can receive data from multiple hospital systems, analyze the data, arbitrate any resulting actions and determine the relative priorities to distribute work to the right person or teams-can lead to improved operational efficiencies and better patient outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0090.008
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.006

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 head0.037
GPT teacher head0.416
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2021
Admission routes1
Has abstractyes

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