621. Reporting Behaviors and Perceptions Towards the National Healthcare Safety Network Antimicrobial Use (AU) and Antimicrobial Resistance (AR) Options
Bibliographic record
Abstract
Abstract Background Antibiotic use (AU) and antibiotic resistance (AR; AUR) reporting to National Healthcare Safety Network (NHSN) is suboptimal by US hospitals. The Society of Infectious Diseases Pharmacists (SIDP) and the Society for Healthcare Epidemiology of America (SHEA) conducted a survey of their membership to 1) Identify characteristics of US health systems that report AUR data 2) Determine how NHSN AUR data are used by health systems and 3) Identify barriers to AUR reporting. Methods An anonymous survey was posted on SurveyMonkey from 1/21- 2/21/2020 and links were emailed to SIDP and SHEA Research Network members. Data were analyzed in Excel and RStudio. Respondent and hospital data were reported as frequencies and percentages. Fisher’s Exact test was used to compare survey responses from NHSN AUR reporters to non-reporters. Results A total of 238 individuals from 43 states responded to our survey. Respondents were primarily pharmacists (84%), from urban (45%), non-profit medical centers (80%) with >250 beds (65%). 62% of respondents reported to the AU option while 19% reported to the AR option. Respondents not using software for local AU or AR tracking were less likely than those using any software for local tracking to report to AU (19% vs 64%) and AR (2% vs 30%) options (P< 0.0001). Among AU and AR reporters 41% and 54% used clinical decision support software to aggregate compile data for upload while 54% and 38% used their electronic health record, and 5% and 8% used another method. Over half of AU (56%) and AR (51%) reporters upload data manually. Regular use of the NHSN data analysis tools was reported by 36% and 9% of those reporting AU and AR data respectively. The most common barriers to reporting were related to technical issues (software, IT support, data formatting) and time/salary support. Among non-reporters, increased expectations to report and better software solutions were most commonly identified as the best ways to increase reporting. Conclusion Efforts to improve AUR reporting should focus on software solutions and increasing the utility of AUR analytical tools. Increasing expectations to report may also help to improve reporting rates. The lower rate of AR vs AU reporting suggests that interventions should also target the AR option. Disclosures Brian J. Werth, PharmD, Shionogi Inc. (Grant/Research Support) Kerry LaPlante, PharmD, Merck (Advisor or Review Panel member, Research Grant or Support)Ocean Spray Cranberries, Inc. (Research Grant or Support)Pfizer Pharmaceuticals (Research Grant or Support)Shionogi, Inc. (Research Grant or Support)
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".