Orchestration of Thick Data Analytics Based on Conversational Workflows in Healthcare Community of Practice
Bibliographic record
Abstract
Every healthcare unit is experiencing tremendous pressure to improve its practice quality across several dimensions. Multiple bodies of literature support the importance of establishing community of practice (CoP) to enrich the professional practice and add the expert context on the patient cases. The CoP emphasizes the importance of qualitative social learning and connectivity as preferred sources of knowledge updates to guide the practice rather than using the mere direct quantitative evidence. Social learning and connectivity in CoP is a complex sociotechnical process that takes an abstract idea through a cycle of participation and reification to derive more thickened context and refined knowledge that will help largely the accuracy of decision making. This process is not a straightforward one requiring the use of suitable hyper structure for representing the contextual evolving knowledge as well as a flexible infrastructure to enable CoP learning from experts, agents and connected services as well as other sources of data and knowledge. This article focuses on using the notion of workflow as the hyper structure and Node-RED as the platform that can facilitate CoP learning and connectivity. The focus is on using the CoP Node-RED workflows in healthcare settings to provide basic collaboration and connectivity as well as extensions to facilitate higher participation, learning, and connectivity to arrive at reification of the practice experience. With Node-RED workflows ideas can be represented as flows and sub flows where it can be shared with other CoP members as JSON hyper structure for further improvement, analytics and decision making.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".