CODEM: a compliance-oriented decomposition method for Québec health system software
Bibliographic record
Abstract
Health professionals and managers face persistent challenges in complying with numerous and broadly defined healthcare regulations. Simultaneously, governments struggle to design, implement, and enforce effective policies. This regulatory gap constrains the operationalization of health system models and governance. Software engineering offers promising tools to mitigate this gap through the application of Regulatory Technology (RegTech) and Supervisory Technology (SupTech). This research introduces the Compliance-Oriented Decomposition Method (CODEM) to enhance adherence to Quebec’s healthcare legislation. It departs from traditional practices by treating legal requirements as raw material, systematically breaking them down into elements that can create compliance indicators and produce digital artifacts such as management dashboards. Using Walt and Gilson’s Policy Triangle Framework, the study hypothesizes that regulations across distinct levels share core components, like actors, spheres, and tasks, which can be decomposed and systematically classified. This hypothesis is evaluated through two case studies involving active healthcare legislation. The extracted elements inform the creation of compliance metrics and a data model, which supports the development of two functional Power BI dashboard prototypes. These dashboards aggregate information by strategic level, task characteristics, legislation type, decision sphere, and other relevant dimensions. Despite acknowledged limitations, the findings demonstrate the feasibility and potential of the proposed method and data model for future research and innovation. Applications include scalable classification via machine learning, user-centric compliance tools, multilevel hierarchical representations, enhanced support for requirements engineering, and digitally structured health policies to improve interoperability.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".