Raising Standards While Watching the Bottom Line Making a Business Case for Infection Control
Bibliographic record
Abstract
While society would benefit from a reduced incidence of nosocomial infections, there is currently no direct reimbursement to hospitals for the purpose of infection control, which forces healthcare institutions to make economic decisions about funding infection control activities. Demonstrating value to administrators is an increasingly important function of the hospital epidemiologist because healthcare executives are faced with many demands and shrinking budgets. Aware of the difficulties that face local infection control programs, the Society for Healthcare Epidemiology of America (SHEA) Board of Directors appointed a task force to draft this evidence-based guideline to assist hospital epidemiologists in justifying and expanding their programs. In Part 1, we describe the basic steps needed to complete a business-case analysis for an individual institution. A case study based on a representative infection control intervention is provided. In Part 2, we review important basic economic concepts and describe approaches that can be used to assess the financial impact of infection prevention, surveillance, and control interventions, as well as the attributable costs of specific healthcare-associated infections. Both parts of the guideline aim to provide the hospital epidemiologist, infection control professional, administrator, and researcher with the tools necessary to complete a thorough business-case analysis and to undertake an outcome study of a nosocomial infection or an infection control intervention.
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 | 0.056 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".