Can a Healthcare "Lean Sweep" Deliver on What Matters to Patients? Comment on "Improving Wait Times to Care for Individuals with Multimorbidities and Complex Conditions Using Value Stream Mapping"
Bibliographic record
Abstract
Disconnects and defects in care - such as duplication, poor integration between services or avoidable adverse events - are costly to the health system and potentially harmful to patients and families. For patients living with multiple chronic conditions, such disconnects can be particularly detrimental. Lean is an approach to optimizing value by reducing waste (eg, duplication and defects) and containing costs (eg, improving integration of services) as well as focusing on what matters to patients. Lean works particularly well to optimize existing processes and services. However, as the burden of chronic illness and frailty overtake episodic care needs, health systems require far greater complex, adaptive change. Such change ought to take into account outcomes in population health in addition to care experiences and costs (together, comprising the Triple Aim); and involve patients and families in co-designing new models of care that better address complex, longer-term health needs.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.070 | 0.063 |
| Insufficient payload (model declined to judge) | 0.013 | 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".