Enhancing board oversight on quality of hospital care
Bibliographic record
Abstract
BACKGROUND: Community hospitals in the United States are almost all governed by a governing board that is legally accountable for the quality of care provided. Increasing pressures for better quality and safety are prompting boards to strengthen their oversight function on quality. PURPOSE: In this study, we aimed to provide an update to prior research by exploring the role and practices of governing boards in quality oversight through the lens of agency theory and comparing hospital quality performance in relation to the adoption of those practices. METHODOLOGY: Data on board practices from a survey conducted by The Governance Institute in 2007 were merged with data on hospital quality drawn from two federal sources that measured processes of care and mortality. The study sample includes 445 public and private not-for-profit hospitals. We used factor analysis to explore the underlying dimensions of board practices. We further compared hospital quality performance by the adoption of each individual board practice. FINDINGS: Consistent with the agency theory, the 13 board practices included in the survey appear to center around enhancing accountability of the board, management, and the medical staff. Reviewing the hospital's quality performance on a regular basis was the most common practice. A number of board practices, not examined in prior research, showed significant association with better performance on process of care and/or risk-adjusted mortality: requiring major new clinical programs to meet quality-related criteria, setting some quality goals at the "theoretical ideal" level, requiring both the board and the medical staff to be as involved as management in setting the agenda for discussion on quality, and requiring the hospital to report its quality/safety performance to the general public. PRACTICE IMPLICATIONS: Hospital governing boards should examine their current practices and consider adopting those that would enhance the accountability of the board itself, management, and the medical staff.
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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 | 0.024 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".