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Record W4407832801 · doi:10.3233/978-1-58603-979-0-248

An EHR-Based Paradigm Shift in the Operation of Mental Health and Addiction Services

2009· book-chapter· en· W4407832801 on OpenAlexaboutno aff
Sara A. Makka

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typebook-chapter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacy and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionParadigm shiftMental healthComputer sciencePsychiatryPsychology

Abstract

fetched live from OpenAlex

This paper responds to a commonly expressed belief, or perhaps hope, that full implementation of the electronic health record (EHR) will promote a “paradigm shift” in the delivery of health services, enhancing both service system efficiency and effectiveness in ways that would not have otherwise been possible. A model is proposed that defines stages in the development of the EHR in terms of two sets of functional components: 1) information management tools used to support the delivery of care; and 2) decision support tools that use information drawn from the EHR to promote functional integration among the components of complex service systems. “Paradigm shift” is defined operationally within this framework in terms of evolution of the EHR through these stages. The concept of “clinical interoperability” (anchored in a semantically interoperable EHR) is elaborated upon and presented as the sine qua non for a distinctive form of paradigm change that centres on support for care delivery within any given location in the system, and on EHR-based support for client movement through the system. The Vancouver Island Health Authority/Infoway Bridges, now deployed across the full array of hospital and community-based mental health and addiction services, is an example of an EHR that leverages the semantically interoperable components of an EHR to support a paradigm shift in clinical interoperability for the mental health and addictions service system.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.919
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.135
GPT teacher head0.493
Teacher spread0.358 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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