Hospital onset bacteremia and fungemia should be a pay-for performance measure: a pro/con debate
Bibliographic record
Abstract
Abstract Healthcare-associated infections (HAIs) result in substantial patient harm and avoidable costs. Pay-for-performance programs (PFP) through the Centers for Medicare and Medicaid Services (CMS) have resulted in reductions of HAIs like central line-associated bloodstream infections (CLABSI) and methicillin-resistant Staphylococcus aureus bacteremia, through robust infection prevention programs and practices. Hospital Onset Bacteremia and Fungemia (HOB) is proposed as an alternative quality measure for public reporting and PFP, and was endorsed by the National Quality Forum in 2022. This broad measure is designed as an electronic quality measure that avoids manual abstraction and excludes risk adjustment. HOB would substantially expand the scope of focus of existing bloodstream infection measurement, and is currently being considered for voluntary reporting in 2025. In this article, we provide arguments for and against adopting HOB as a PFP measure linked to CMS payments.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".