MétaCan
Menu
Back to cohort
Record W1570376970

Cross-Canada EMR Case Studies: Analysis of Physicians' Perspectives on Benefits and Barriers

2011· article· en· W1570376970 on OpenAlexaffabout
Grace I. Paterson, Nicola Shaw, Andrew Grant, É Delisle, Kevin J. Leonard, Shelby Mitchell Corley, Maryan McCarrey, Bill Pascal, Nancy Kraetschmer

Bibliographic record

VenueeJournal of health informatics · 2011
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsCanadian Medical AssociationDalhousie UniversityAlgoma UniversityUniversity of TorontoUniversity of AlbertaUniversité de Sherbrooke
Fundersnot available
KeywordsElectronic medical recordBest practiceElectronic health recordMedical recordHealth careMedicineMedical educationFamily medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Objective: Our objective was to provide physicians with practical information on best practices and lessons learned with regards to implementation and use of electronic medical record (EMR) systems in ambulatory clinical practice settings. Methodology: A cross-Canada EMR study—the first of its kind—used case study methodology to investigate how EMRs were implemented and used in primary care. Knowledge transfer methods included print and web publications by the Canadian Medical Association (CMA) and a workshop. Results: The 20 case studies informed us in detail of the critical success factors for implementation. These were validated and augmented through a workshop. Conclusions: Electronic medical record (EMR) uptake in Canada and the US significantly lags behind other countries. Hence, there is a need to spread the good news about the actual benefits of EMRs to patients, physicians and the health care system and to mitigate barriers to EMR adoption and use.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.102
GPT teacher head0.444
Teacher spread0.343 · 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 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

Citations7
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueeJournal of health informaticsSame topicElectronic Health Records SystemsFrench-language works237,207