Bibliographic record
Abstract
While the structure of healthcare systems evolved out of the need to address acute conditions, the function of healthcare systems evolved to primarily address chronic conditions. The healthcare delivery system organically developed to respond to "one-off" acute illness or injury. Subsequently, healthcare delivery systems grew into legacy systems that evolved into complex systems over time. Healthcare delivery for acute conditions tends to utilize a specific part or form of the healthcare delivery system. In contrast, healthcare delivery for chronic conditions forces patients to seek care over time between different places or healthcare entities. Because of the self-contained structural organization of these healthcare delivery systems, they were not designed to provide coordinated, integrated, and longitudinal care over time and place. Consequently, today's complex legacy healthcare delivery system requires significant improvement in the quality of care delivered to patients, especially those with chronic conditions. As a complex and legacy system, the most appropriate approach to improve the quality of delivered care is through a re-design quality improvement process, rather than a new system design process. In this paper, we describe the conceptual framework for quality improvement (QI) and the current micro and macro level approaches to quality improvement. We applied the current quality improvement approaches to the QI conceptual framework. We identified the limitations in current quality improvement processes in complex healthcare systems at the macro-level, pointing to the need for macro-systems approaches to healthcare quality improvement.
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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".