Tracking and Reporting Antimicrobial Use: Development of an Antimicrobial Stewardship Dashboard
Notice bibliographique
Résumé
Abstract Corresponding author: Kathryn Timberlake, 555 University Ave, Toronto, ON M5G1X8, 416-813-6475, Kathryn.timberlake@sickkids.ca No conflicts of interest. Background Tracking and reporting antimicrobial use (AU) is a core element of the Centers for Disease Control and Prevention recommendations for Antimicrobial Stewardship Programs (ASP). Antimicrobial consumption measured in standardized units such as days of therapy (DOT) per 1000 patient days is a common metric used to identify areas for improvement and assess the impact of antimicrobial stewardship. Additionally, benchmarking between units, services, most responsible physicians (MRPs), and with external hospitals is feasible when AU data is available. Adoption of electronic health records provides an opportunity to leverage available data to generate these metrics. Objective was to design, develop, and validate an AU dashboard for use by an established ASP at a large pediatric academic hospital to facilitate internal and external reporting. Methods The setting was The Hospital for Sick Children, Toronto, Canada. The design of the AU dashboard was developed with input from members of the ASP steering committee, pharmacy, and utilization management committee. Data source was SickKids Enterprise-wide Data in Azure Repository (SEDAR), which receives, filters, curates and validates Epic data daily including medication administration records (MAR). All antimicrobial administrations given to inpatients were identified and classified according to the American Hospital Formulary Service. The first dose of a medication given on a calendar day was assigned one DOT. Antimicrobial administrations were allocated to clinical units and MRPs at specified times each day. The DOT, patient days, and DOT per 1000 patient days were then calculated and visually displayed on a Power BI dashboard. Validation of all data in the report including identification of missing information was conducted. Discrepancies from admission, discharge, transfer, and MRP designation were evaluated. Results We successfully developed the dashboard and validated the data on antimicrobial prescribing by units and MRPs, allowing for comparisons. Units and services with relatively few patient days were often difficult to compare as AU per 1000 patient days lacked precision. Therefore, for comparison, we removed units and services with less than 1000 patient days per year. The dashboard displayed the following: “top 10” units and services in tabular and bar graph formats, AU over time, and cumulative drug use by unit or service in a stacked bar graph. Previous data extracts for annual reporting and benchmarking from the health information system reports took hours to days to compile, but now can be completed in 60-90 minutes. Antimicrobial administration data have been submitted to Public Health Ontario (PHO) for their AU Comparison Tool for benchmarking antimicrobial stewardship and antibiotic use across the province. Additionally, we reported AU to the Canadian Nosocomial Infection Surveillance Program (CNISP) where AU and antimicrobial resistance trends are being explored. Data are now readily available for trending AU to assess the impact of ASP interventions. Conclusion The development and validation of an AU Dashboard using a curated health information system data source (SEDAR) has improved the accuracy, efficiency, and timeliness of AU reporting. Future directions for this work include benchmarking between other pediatric hospitals, developing a dashboard for antimicrobial cost, and identification of future ASP projects. Sharing our findings may help other hospitals who aim to have AU dashboards improve their reports. Future efforts will include reporting AU by MRPs in a de-identified fashion so that physicians can benchmark their own practice. Figure 1. Dashboard Overview
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,047 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».