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Record W2931378573 · doi:10.24251/hicss.2019.489

Enabling laboratory medicine in primary care through EMR systems use: A survey of Canadian physicians

2019· article· en· W2931378573 on OpenAlexaffabout
Louis Raymond, Guy Paré, Éric Maillet

Bibliographic record

VenueProceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversité de SherbrookeHEC MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPrimary careElectronic medical recordMedicineQuality (philosophy)Medical recordPatient careMedical laboratoryFamily medicineMedical educationMedical emergencyNursing

Abstract

fetched live from OpenAlex

Important problems remain with regard to the efficiency and effectiveness of laboratory testing in primary care. In view of this, a significant function of electronic medical record (EMR) systems is to enable the practice of laboratory medicine by primary care physicians (PCPs). In addressing this issue, the present study aims to deepen our understanding of the nature and effectiveness of PCPs’ use of EMR systems for patient management and care within the laboratory testing process. To achieve our main objective, a survey of 684 Canadian physicians was realized. Results confirm that the artefactual and clinical contexts of EMR use influence the extensiveness of this use for communicational and clinical purposes. In turn, it is confirmed that the more extensive the use of EMR for laboratory medicine, the greater its impacts on the PCPs’ efficiency and on the quality of care provided by these physicians. The implications of these results are discussed.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0050.001
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 head0.096
GPT teacher head0.345
Teacher spread0.249 · 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.

Study designQualitative
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
Published2019
Admission routes2
Has abstractyes

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