621. Reporting Behaviors and Perceptions Towards the National Healthcare Safety Network Antimicrobial Use (AU) and Antimicrobial Resistance (AR) Options
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».