Volume-based credentialing: Practical steps to promote high quality, safe care
Bibliographic record
Abstract
All hospitals engage in credentialing to evaluate the qualifications of practitioners who request clinical privileges. Credentialing always includes verifying an applicant’s education, training, licensure and board certification, and evaluating information provided by references who have worked with the applicant. Far fewer hospitals consider whether a practitioner’s volume of cases is sufficient to demonstrate proficiency in a specialty area. This is surprising, given the well-established relationship in the medical literature between volume and proficiency. The authors identified several reasons for hospitals’ reluctance to use volume-based credentialing. These include the fear of lawsuits by physicians and the practical difficulties of satisfying volume requirements in smaller hospitals with fewer patients. The authors conclude that legal challenges to volume-based credentialing are unlikely to be successful and that techniques exist to enable small hospitals to use volume-based credentialing to promote high quality and safe care.
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 | 0.046 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".