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Record W2059346558 · doi:10.1504/ijeb.2004.006129

E-health: applying business process reengineering principles to healthcare in Canada

2004· article· en· W2059346558 on OpenAlexafffundabout
Michael Bliemel, Khaled Hassanein

Bibliographic record

VenueInternational Journal of Electronic Business · 2004
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Waterloo
KeywordsBusiness process reengineeringHealth careProcess (computing)Business processProcess managementBusinessKey (lock)Healthcare deliverySet (abstract data type)Computer scienceRisk analysis (engineering)Knowledge managementComputer securityWork in processMarketingPolitical science

Abstract

fetched live from OpenAlex

Healthcare in Canada is facing many problems. The most publicised symptoms are excessive waiting times for patients, lack of access, high cost of delivery and medical errors. e-Health has been introduced as a potential solution for such problems. This research will explore the area of e-health and the technologies as well as the concepts that are included under its large umbrella. Bearing in mind that e-health is more than a set of technological applications, a business process reengineering (BPR) framework will be used to examine the application of particular BPR principles to address specific problems that are plaguing the Canadian healthcare system. The framework identifies the e-health technologies and processes that could best support the effective application of these BPR principles within a healthcare environment, as well as the key barriers impeding their implementation.

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)0.004
metaresearch head score (Gemma)0.011
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: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0080.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.387
Teacher spread0.350 · 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

Citations35
Published2004
Admission routes3
Has abstractyes

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