Incorporating business process management, business ontology and business architecture in medication management quality
Bibliographic record
Abstract
Managers and care providers in the health sector are expected to deliver safe, efficient and effective services within a resource constrained, complex system. Services are provided through execution of multiple processes. Healthcare organizations tend to be structured in functional based silos with process improvement efforts often focused on individual processes within the discrete silos. This silo based improvement approach fails to take into account upstream and downstream processes executed and managed in other silos. A patient’s journey will typically include processes from multiple silos and therefore, improvement efforts need to focus on end-to-end processes if the goal is to deliver a positive patient experience. In order to optimize processes in a complex adaptive system like healthcare and to effect meaningful change a combination of management disciplines is required. This research explored the use of Business Process Management (BPM), Business Architecture (BA) and Business Process Management Ontology (BPMO) as a comprehensive, integrated approach to design, redesign, evaluate, improve and monitor the safety, efficiency and effectiveness of medication management processes in a multi-site healthcare organization. The contribution of the research was threefold. First, identified benefits of applying BPM, BPMO and BA to increase organization capacity and improve the end-to-end process of medication management; second, demonstrated the application of an ontology and the business layer of enterprise architecture used in other sectors could be successfully utilized in the healthcare sector; and third, developed a process reference model for medication management processes in acute care and long term care facilities.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".