Benefits and limitations of business process model notation in modelling patient healthcare trajectory: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The adoption of business process model notation (BPMN) in modelling healthcare trajectory can enhance the efficiency and efficacy of healthcare organisations, improve patient outcomes while restraining costs. Existing systematic reviews have been inconclusive regarding the effectiveness of BPMN in modelling healthcare trajectory. The aims of this scoping review are to map and aggregate existing evidence on the benefits and limitations associated with BPMN in healthcare trajectory, highlighting areas of improvement on BPMN and its extensions in healthcare. We will assess BPMN's ability to model key dimensions or concepts of the healthcare process and to meet the needs of stakeholders. The review will highlight the advantages of this approach to support clinical activities and decision-making processes associated with the healthcare trajectory, proposing a conceptual framework for improving the use of BPMN in healthcare. METHODS AND ANALYSIS: This study will be performed in accordance with the methodological framework suggested by Arksey and O'Malley. A wide range of electronic databases and grey literature sources will be systematically searched using predefined keywords. The review will include any study design focusing on the application of the BPMN approach for optimising healthcare trajectories, published in either English or French from 1 January 2004 to 9 December 2021. Two reviewers will independently screen titles, abstracts and full-text articles and select articles meeting the inclusion criteria. A customised data extraction form will be used to extract data. The results will be presented using descriptive statistics and thematic analysis on qualitative data. ETHICS AND DISSEMINATION: Research ethics approval is not required. Review findings will be used to advance understanding about BPMN, its extensions and application in healthcare trajectory optimisation. The review will develop recommendations on tailoring BPMN strategies for optimising care pathways and decision-making processes. Findings will be disseminated in peer-reviewed journals, conferences and discussions with relevant organisations and stakeholders.
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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 | 0.228 | 0.224 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".