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Record W7124531553 · doi:10.5281/zenodo.18293372

Health Information Exchange: Engaging Providers in Health Care Innovation

2017· report· en· W7124531553 on OpenAlexaffabout
Rabi Doreen, Belal Chemali

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typereport
Languageen
Field
Topic
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsHealth information exchangeHealth careGovernment (linguistics)StakeholderHRHISInformation exchangeHealth policyHealth informationInformation system

Abstract

fetched live from OpenAlex

This commissioned discussion paper explores the adoption and implementation of digital health information exchange (HIE) and integrated health records in Alberta, Canada. The report synthesizes evidence from peer-reviewed literature, government reports, and case studies to evaluate the current state of health information sharing, identify barriers to provider engagement, and assess the potential benefits for patient-centered care, provider efficiency, and system-wide outcomes. The authors conducted a structured review of empirical studies, policy analyses, and stakeholder feedback, providing actionable recommendations to guide the development of a province-wide IHR. This research-informed report emphasizes the importance of professional standards, technical interoperability, and strategic planning to facilitate secure, comprehensive, and effective health information exchange across the continuum of care.

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)0.067
metaresearch head score (Gemma)0.054
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 score0.515
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.054
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0140.016
Scholarly communication0.0240.012
Open science0.0030.015
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.080
GPT teacher head0.336
Teacher spread0.256 · 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
Published2017
Admission routes2
Has abstractyes

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