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Record W2137422265 · doi:10.1136/amiajnl-2011-000560

Multidimensional evaluation of a radio frequency identification wi-fi location tracking system in an acute-care hospital setting

2012· article· en· W2137422265 on OpenAlexafffund
B. Okoniewska, Andrew J. Graham, Marina L. Gavrilova, D. Wah, J. Gilgen, J. Coke, John Burden, Shifali Nayyar, Jameson Kaunda, Dean Yergens, Barry Baylis, William A. Ghali

Bibliographic record

VenueJournal of the American Medical Informatics Association · 2012
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of TorontoAlberta Health ServicesUniversity of Calgary
FundersCanada Research ChairsAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsReal-time locating systemRadio-frequency identificationTracking (education)Computer scienceSituation awarenessIdentification (biology)Health careScale (ratio)TelemedicineTracking systemAsset (computer security)Real-time computingArtificial intelligenceComputer securityEngineeringCartographyGeography

Abstract

fetched live from OpenAlex

Real-time locating systems (RTLS) have the potential to enhance healthcare systems through the live tracking of assets, patients and staff. This study evaluated a commercially available RTLS system deployed in a clinical setting, with three objectives: (1) assessment of the location accuracy of the technology in a clinical setting; (2) assessment of the value of asset tracking to staff; and (3) assessment of threshold monitoring applications developed for patient tracking and inventory control. Simulated daily activities were monitored by RTLS and compared with direct research team observations. Staff surveys and interviews concerning the system's effectiveness and accuracy were also conducted and analyzed. The study showed only modest location accuracy, and mixed reactions in staff interviews. These findings reveal that the technology needs to be refined further for better specific location accuracy before full-scale implementation can be recommended.

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)0.023
metaresearch head score (Gemma)0.006
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 score0.078
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.419
Teacher spread0.389 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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