An EHR-Based Paradigm Shift in the Operation of Mental Health and Addiction Services
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".