Revolutionizing Healthcare System through Lean Thinking
Bibliographic record
Abstract
Healthcare providers are facing increasing pressure to improve service, reduce costs, improve patient safety, reduce waiting times, and reduce errors and associated litigation. The United States spends 22% more than second-ranked Luxembourg, 49%more than third-ranked Switzerland on healthcare per capita, and 2.4 times the average of other OECD countries. In Ontario, healthcare will account for 50% of governmental spending by 2011, two-thirds by 2017, and 100% by 2026 unless a radical approach to healthcare is adopted. In China, 39% of rural and 36% of urban population cannot afford professional medical treatment despite the success of the country's economic and social reforms over the past 25 years (OECD Health Data, 2006). Clothier (2006) estimated that 50% of a clinician's time is not necessary and non-value added in the eyes of the patient. And as per Nino,V. et al., (2021) patient satisfaction can be availed by reducing the delays in registration process. By simplifying processes, lean thinking and lean principles can assist in removing wasteful motion, waiting, and other non-value-added tasks (Wickramasinghe, 2014).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".