Consensus recommendations for incident learning database structures in radiation oncology
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Notice bibliographique
Résumé
PURPOSE: Incident learning plays a key role in improving quality and safety in a wide range of industries and medical disciplines. However, implementing an effective incident learning system is complex, especially in radiation oncology. One current barrier is the lack of technical standards to guide users or developers. This report, the product of an initiative by the Work Group on Prevention of Errors in Radiation Oncology of the American Association of Physicists in Medicine, provides technical recommendations for the content and structure of incident learning databases in radiation oncology. METHODS: A panel of experts was assembled and tasked with developing consensus recommendations in five key areas: definitions, process maps, severity scales, causality taxonomy, and data elements. Experts included representatives from all major North American radiation oncology organizations as well as users and developers of public and in-house reporting systems with over two decades of collective experience. Recommendations were developed that take into account existing incident learning systems as well as the requirements of outside agencies. RESULTS: Consensus recommendations are provided for the five major topic areas. In the process mapping task, 91 common steps were identified for external beam radiation therapy and 88 in brachytherapy. A novel feature of the process maps is the identification of "safety barriers," also known as critical control points, which are any process steps whose primary function is to prevent errors or mistakes from occurring or propagating through the radiotherapy workflow. Other recommendations include a ten-level medical severity scale designed to reflect the observed or estimated harm to a patient, a radiation oncology-specific root causes table to facilitate and regularize root-cause analyses, and recommendations for data elements and structures to aid in development of electronic databases. Also presented is a list of key functional requirements of any reporting system. CONCLUSIONS: Incident learning is recognized as an invaluable tool for improving the quality and safety of treatments. The consensus recommendations in this report are intended to facilitate the implementation of such systems within individual clinics as well as on broader national and international scales.
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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 | 0,000 | 0,000 |
| 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 découle