Modeling and Verifying Timed Compensable Workflows and an Application to Health Care
Bibliographic record
Abstract
Over the years, researchers have investigated how to provide better support for hospital administration, therapy and laboratory workflows. Among these efforts, as with any other safety critical system, reliability of the workflows is a key issue. In this paper, we provide a method to enhance the reliability of real world workflows by incorporating timed compensable tasks into the workflows, and by using formal verification methods (e.g., model checking). We extend our previous work [1] with the notion of time by providing the formal semantics of Timed Compensable WorkFlow nets (CWFT -nets). We extend the graphical modeling language of Nova WorkFlow (a workflow management system currently under development) to model CWFT -nets and enhance Nova WorkFlow's automatic translator to translate a CWFT -net into DVE, the modeling language of the distributed LTL model checker DiVinE. These enhancements provide a method for rapid (re)design and verification of timed compensable workflows. We present a real world case study for Seniors' Care, developed through collaboration with the local health authority.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".