A methodology for modeling healthcare teams and an evaluation of Business Process Modeling Notation as a Modeling Language
Bibliographic record
Abstract
Whether it is offering services, delivering solutions or driving innovations, team work has been a hallmark of efficiency and effectiveness in various industries. The healthcare industry is not left out as its service delivery process involves numerous interfaces, information flows and patient hand-offs among professionals with different educational training, differing knowledge levels and possibly working from different locations as well. As healthcare delivery evolves to being more patient-centered, so does the team settings as well, becoming more collaborative. Such changes also translate into a need for support systems to evolve to be able to provide support for the extent of collaboration that would be needed. A framework is needed to guide in the development of such systems. However, due to the varying needs of patients, team types and make-up would generally differ, so we explored the different types of team settings studying what they entail based on their various degrees of collaboration. We therefore present in this thesis a model of team based concepts, an ontology formalizing the model, team based scenarios designed using the ontology and then application of the scenarios to test the ability of BPMN (Business Process Modeling Notation) to model healthcare teams.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".