Benefits and Limitations of Business Process Model Notation in Modeling Patient Healthcare Trajectory: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Introduction: The adoption of business process model notation (BPMN) in modeling healthcare trajectory can enhance the efficiency and efficacy of healthcare organizations and ultimately improve patient outcomes while restraining costs. However, existing systematic reviews have been inconclusive regarding the effectiveness of BPMN in modeling healthcare trajectory. The aims of this scoping review are to map and aggregate existing evidence on the main benefits and limitations associated with BPMN in healthcare trajectory and highlight areas of improvement on using BPMN and its extensions in healthcare practices, which have not been systematically scoped. Methods and Analysis : The proposed scoping review will be performed in accordance with the methodological framework suggested by Arksey and O’Malley and further refined by Levac et al. A wide range of electronic databases and grey literature sources will be systematically searched using predefined keywords, from 2004 onwards. The review will include any study design with a focus on the application of the BPMN approach applied for optimizing healthcare trajectories (e.g., diagnostic, and therapeutic process, decision making, cost, and resources), published in either English or French. Two reviewers will independently screen titles, abstracts, and full-text articles and select studies meeting the inclusion criteria. A customized data extraction form will be used to extract data from the included studies. The results will be presented in tabular format developed iteratively by the research team. Ethics and dissemination : Research ethics approval is not required as exclusively secondary data will be used. Review findings will be used to advance understanding about BPMN, its extensions and its application in healthcare trajectory optimization. The review will develop recommendations about how to tailor BPMN strategies at optimizing care pathways and decision-making processes. Our findings will be disseminated in peer-reviewed journals and presentations and through discussions with relevant organizations and stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.375 | 0.410 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".