Relationship-centered health care as a Lean intervention
Bibliographic record
Abstract
Continuous improvement efforts, recognized in much literature as Lean management techniques have been used in efforts to improve efficiency in democratic health care contexts for some time to varying degrees of success. The complexity of the health care system is magnified by the sheer number of processes and sub processes required to deliver value within a bureaucratic environment, while maintaining some level of compassionate and personalized care. There is inherent tension between what is required to be efficient and what is required to be caring and this conflict presses against Lean practice at the level of delivery.Administration and care intersect at the point of the patient's experience. In order to achieve the dual goals of improved value and lower costs, the application of Lean thinking for meaningful health care reform must acknowledge the fundamental dichotomy between the impersonal tasks required to provide health services, and human interactions. Meaningful health care reform requires an acknowledgement of this distinction, currently not recognized in literature. While administrative process improvements are necessary, they are insufficient to achieve a sustainable and caring health care system. Lean thinking must be applied differently for administrative processes and patient care encounters, because these are fundamentally different processes. In this way, Lean principles will effectively contribute to sustainable health system improvements.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".