Reference Architecture Frameworks for Chronic Disease Management Solutions
Bibliographic record
Abstract
Background: Chronic diseases have beaten acute and infectious diseases as Canada's primary cause of illness, disability, and death. Considering the growing and aging population, chronic diseases are expected to climb in the short term. As a result, to achieve the best outcomes possible for those living with chronic diseases and minimize the cost burden on the health and social care systems, effective chronic disease management solutions are critical. Aim: This project aimed to establish a general reference architecture that offers guidelines, including the business processes and information required for developing the chronic disease management solutions as a white-label platform, prompting the quality and interoperability with existing e-health systems, low coupling, and reusability. Methods: The Department of Defense Architecture Framework (DODAF) and its suggested methodology were used to create the architecture descriptions, viewpoints, and models required for building our general reference architecture for chronic disease management solutions. Analysis was conducted based on PubMed articles published from 2002 to the present. The key terms used to filter the searches were “Chronic disease model,” “Chronic care model,” “Chronic disease management infostructure,” “Chronic disease management system,” or “Chronic disease management guidelines.” Outcomes: Based on the research study done around the chronic disease management guidelines, standards, care models, and infostructure, the following outcomes suggested by DODAF were built to shape the business and information architecture frameworks: capability model, operational activity model, conceptual data model, logical data model, and the maps between the capabilities, operational activities, and data entities. Conclusion: The development of a general reference business and information architecture frameworks targeting all chronic conditions will facilitate the development of future systems and services in the domain of chronic disease management through the advantages that come with the architecture-based modeling approach offered by this project such as capability-driven system development, interoperability, reusability, semantically unambiguous descriptions, and comprehensive specifications.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".