Structure of Antimicrobial Stewardship Programs in Leading U.S. Hospitals
Bibliographic record
Abstract
Antibiotic use has drastically changed the course of modern medicine. However, the overuse and often inappropriate use of antibiotics has led to the development of resistant strains of bacteria. Increasingly, healthcare systems struggle to deal with the burden of fighting infections that no longer respond to common antibiotic-based treatments. One strategy used to combat antibiotic resistance is the implementation of hospital-based Antimicrobial Stewardship Programs (ASP). ASP structure among the top U.S hospitals may provide insight into which of the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA) ASP recommendations are most efficacious given limited resources. We thus administered a survey to better understand the elements of an ASP that are utilized at these top-rated hospitals. We surveyed the 50 highest ranking hospitals in various specialties using the 2015-2016 U.S News lists of top hospitals. This corresponded to 137 adult and 70 pediatric sites. We inquired as to which components of the 2016 IDSA and SHEA ASP guidelines were implemented at each site. Appropriate persons at each hospital were contacted by telephone and email. Overall, 102 of 207 hospitals responded (49.3%). Of these 87.2% had an active ASP, and 57.1% were active for more than 5 years. Interventions most widely adopted included prospective auditing of antimicrobial usage (n = 65, 87.8%), pre-authorization of antimicrobials (n = 61, 82.4%), and antimicrobials restricted to infectious disease physicians (n = 52, 70.3%). The most widely implemented optimization strategies included promoting transition from intravenous to oral antibiotics (n = 68, 93.2%) and strategies to minimize antimicrobial therapy duration (n = 56, 76.7%). The least common interventions included antimicrobial time-outs (n = 17, 23.0%) and ASP intervention in cases with high risk of Clostridium Difficile infection (n = 27, 36.5%). The least common optimization strategy was the use of time-sensitive stop orders (n = 27, 37.0%). Most leading U.S hospitals selectively implement IDSA and SHEA recommendations. Understanding the structure of ASPs in these hospitals will assist other hospitals in implementing their programs. All authors: No reported disclosures.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".