Aligning Incentives for Academic Physicians to Improve Health Care Quality
Bibliographic record
Abstract
ACADEMIC HEALTH SCIENCE CENTERS PLAY A LEADing role in the development of new knowledge, yet the quality of care in academic hospitals is on average only modestly better than that of care provided by community hospitals. However, averages are also misleading. In one study evaluating care of patients with acute coronary syndrome, 15 of the 20 top-performing institutions were community hospitals. Indeed, many organizations that have been at the forefront of the quality improvement movement, such as Intermountain Healthcare and Geisinger Health System, are not among the highest-ranked academic centers in terms of research grants and research productivity. One reason academic health science centers do not consistently provide superior care may be the incentive structures that exist within them for academic physicians. These physicians determine not only what research is undertaken but also how clinical care is delivered and how much attention is given to quality improvement. The choices that academic physicians make, and the incentives that affect those choices, have a profound influence not only on knowledge generation but also on the quality of health care received by millions of patients. In this Commentary, we suggest that academic physicians currently face financial and nonfinancial incentives that discourage the expenditure of time and energy on projects likely to improve patient care in their local environments. These incentives will need to change if academic health science centers are to become leaders in quality improvement.
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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 | 0.076 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".