Bibliographic record
Abstract
Countries around the globe are struggling with the rising cost of delivering health care. In the developed world, this trend is enforced by aging demographics and emerging forms of expensive medical interventions. Disease prevention, early disease detection, and evidence- based disease management are key for keeping health care systems sustainable. Electronic information management has been recognized as a central enabler for increasing the quality of health care while controlling the cost of delivering it. Secondary care facilities (e.g., hospitals) and laboratories have made use of electronic information systems for decades. However, the primary care sector has only recently begun to adopt such systems on a broader scale. The benefit provided by each system in isolation is limited since citizens generally receive their care from a multitude of providers. Health care information systems need to interoperate in order to enable integrated health information management and consequently attain the declared qualitative and economic objectives. Many industrial countries have begun to create common infrastructures for such an integrated electronic health record (EHR) (Blobel, 2006). Different approaches exist, ranging from centralized databases to highly distributed collections of mediated provider-based systems. This chapter describes the architecture of the Canadian infrastructure for health information management, which can be seen as a compromise between a fully centralized and a fully distributed solution. While in Canada the delivery of health care is a matter of provincial territorial authority, the health ministers of all provinces and the federation have created a joint organization called Health Canada Infoway with the mandate to develop an architecture for and foster implementation of a joint interoperability infrastructure for EHRs in Canada. The second major version of this architecture has now been released, and provinces have begun to implement it. The solution is based on the paradigm of a service-oriented architecture (SOA) (Erl, 2004) and embraces a range of domain-specific and technical standards. It leverages and integrates existing investments in health information systems by making them available through interface standards-conform interface adapters. The Canadian EHR architecture has received attention beyond the Canadian context. This chapter reports on this architecture, its enabling technology paradigms, experiences with its implementation, and its limitations.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".