Quality, Value, Accountability and Information as Transforming Strategies for Patient-Centred Care: A Commentary from an International Perspective
Bibliographic record
Abstract
The reviewed paper describes how one of the largest integrated healthcare systems in the US has successfully transformed itself to provide 21st-century healthcare. Even though there are other examples of successful transformation of public health services, it is difficult for large, bureaucratic systems to change, and a substantial number of ministries of health or social security health systems in the developing world are run under the assumption that change is very difficult if not impossible to achieve. The VA has significant differences and more financial resources compared to most of the developing world public health institutions; but still, change is often not only about money, but also about strategic direction, commitment and leadership. On the basis of the main strategies used by the VA in its transformation process, the author makes some comments and suggestions for improving developing world healthcare organizations through lessons learned from the VA management strategies. Demand-driven or patient-centred systems are key for success and for the buy-in and involvement of the population and users of healthcare services, but this is easier said than done, especially in developing healthcare systems with immature information systems, access mechanisms and knowledge management. There is a belief in general that large bureaucratic organizations have a hard time adapting and transforming in response to the rapid change of society, technology and most importantly the needs and expectations of their users. The article describes how the largest integrated healthcare organization in the United States, the Veterans Health Administration, has undertaken changes that have turned it into a modern, well-managed organization that outperforms its competitors and has significantly increased its efficiency and users' satisfaction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".