Rationale and development of a business case for antimicrobial stewardship programs in acute care hospital settings
Bibliographic record
Abstract
Abstract Background Antimicrobial stewardship programs (ASPs) have been shown to reduce inappropriate antimicrobial use and its consequences. However, these programs lack legislative requirements in many places and it can be difficult to determine what human resources are required for these programs and how to create a business case to present to hospital administrators for program funding. The objectives of the current paper were to review legislative requirements and outline human resource requirements for ASPs, and to create a base business case for ASPs. Methods A working group of antimicrobial stewardship experts from across Canada met to discuss the necessary components for creation of a business case for antimicrobial stewardship. A narrative review of the literature of the regulatory requirements and human resource recommendations for ASPs was conducted. Informed by the review and using a consensus decision-making process, the expert working group developed human resource recommendations based on a 1000 bed acute care health care facility in Canada. A spreadsheet based business case model for ASPs was also created. Results Legislative and /or regulatory requirements for ASPs were found in 2 countries and one state jurisdiction. The literature review and consensus development process recommended the following minimum human resources complement: 1 physician, 3 pharmacists, 0.5 program administrative and coordination support, and 0.4 data analyst support as full time equivalents (FTEs) per 1000 acute care beds. Necessary components for the business case model, including the human resource requirements, were determined to create a spreadsheet based model. Conclusions There is evidence to support the negative outcomes of inappropriate antimicrobial use as well as the benefits of ASPs. Legislative and /or regulatory requirements for ASPs are not common. The available evidence for human resource recommendations for ASPs using a narrative review process was examined and a base business case modelling scenario was created. As regulatory requirements for ASPs increase, it will be necessary to create accurate business cases for ASPs in order to obtain the necessary funding to render these programs successful.
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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 | 0.096 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| 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".