1365Identifying Opportunities to Improve Environmental Hygiene in Multiple Healthcare Settings
Bibliographic record
Abstract
Background. Near-patient surfaces play a role in transmission of pathogens in healthcare settings. Thus, disinfection cleaning is an important infection prevention intervention. Our previous studies objectively documented opportunities to improve environmental cleaning in acute care hospitals. We used the same evaluation system to analyze cleaning practice in a range of defined healthcare venues. Methods. Trained healthcare professionals, primarily infection preventionists and hospital epidemiologists, in 140 facilities (121 acute care hospitals and 19 long-term care facilities) covertly evaluated disinfection cleaning practice using a fluorescent targeting system (DAZO®) to objectively quantify cleaning compliance of standardized sets of near-patient surfaces that had a high risk of transmitting pathogens between patients and healthcare workers. The objects chosen were specific to the particular venue evaluated. Results were expressed as the percentage of surfaces marked with the fluorescent target that were cleaned (DAZO® removed). Results. As summarized in the figure, thoroughness of discharge cleaning of 52,931 objects in 4,243 medical/surgical and ICU rooms averaged 49% (95% CI = 48.1 to 51.0). Thoroughness of daily cleaning of: 3,657 objects in 271 implantation operating rooms was 24%; 1,160 objects in 84 adult ICU rooms was 26%; 3,680 objects in both common areas and patient rooms in long-term care facilities was 24%; and 610 objects in 38 ambulatory clinic treatment areas was 20%. While potentially overestimated as a result of a Hawthorne effect, daily cleaning, which averaged 25%, was significantly less thorough than discharge cleaning (p = <.0001). Conclusion. The thoroughness of disinfection cleaning was surprisingly similar in the 129 facilities evaluated. Covert evaluation of disinfection cleaning of both inpatient and outpatient care areas consistently revealed opportunities for practice improvement. These findings were also similar to affiliated studies in Canada and Australia and they provided an objective basis for subsequent successful process improvement projects in all sites that implemented structured programs to enhance the thoroughness of cleaning practice. Disclosures. P. Carling, Ecolab: Patent License and Speaker's Bureau, Consulting fee and Licensing agreement or royalty S. S. Huang, Sage Products: Conducting clinical trial for which contributed product is being provided to participating hospitals, Contributed Product; Molnlycke: Conducting clinical trial for which contributed product is being provided to participating hospitals, Contributed product
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".