Location-aware business process management for real-time monitoring of a cardiac care process
Bibliographic record
Abstract
Long wait times are a global issue in the Canadian healthcare system. Patient flow management relies on flow managers to manually detect, investigate and mitigate wait time issues. However, existing data that could support this activity is usually not accurate (because of possible human errors), incomplete, late, and scattered across various information systems in a typical hospital. Yet, in the case of cardiac patients, ensuring a prompt, smooth and continuous care delivery becomes extremely important and motivates improvement of data support for patient flow management activities. This paper presents the development of a location-aware business process management system (LA-BPMS) for monitoring a cardiac care delivery process in a hospital and in real-time. The system provides a better visibility of process execution to patient flow managers who can rely on accurate and real-time information about patient process states, as well as wait time measurements to control patient flow efficiently. We show how an intelligent approach of combining location awareness and business process automation allow this to be possible. A real cardiac care process from an Ontario hospital is used as an example.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".