A Systematic Review of Interventions to Reduce Computed Tomography Usage in the Emergency Department
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Study objectiveUnnecessary computed tomography (CT) scans burden the health care system, leading to increased emergency department (ED) wait times and lengths of stay, costing almost a billion dollars annually. This study aimed to describe ED-based interventions that are most effective at reducing CT imaging while maintaining diagnostic accuracy and patient safety.MethodsAdhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, MEDLINE, Embase, CINAHL, Cochrane Central Register of Controlled Trials, and Google Scholar were searched until December 31, 2020. Randomized and nonrandomized studies that assessed the effect of an ED-based intervention on CT scan usage were included. Abstract screening, data extraction, and quality assessment were conducted in duplicate. The Grading of Recommendation Assessment, Development and Evaluation framework, with the Risk of Bias 2 and Risk of Bias in Nonrandomized Studies - of Interventions tools, was used to determine the certainty of evidence. Significant clinical and statistical heterogeneity precluded meta-analysis; hence, a narrative synthesis was conducted.ResultsA total of 149 studies were included of 5,667 screened abstracts, with substantial interrater reliability among reviewers (Cohen’s κ>0.60). The CT reduction strategies were categorized into 15 single and 11 multimodal interventions by consensus review. Interventions that consistently reduced CT usage included diagnostic pathways, alternative test availability, specialist involvement, and provider feedback. Family/patient education, clinical decision support tools, or passive guideline dissemination did not consistently reduce usage. Only 44% of studies reported unintended consequences of reduction strategies; however, these showed no increase in missed diagnoses or patient harm. The interventions that engaged multiple specialties during planning/implementation had a greater reduction effect than ED only. The certainty of evidence for the primary outcome was very low.ConclusionMultidisciplinary-led interventions that provided an alternative to CT imaging were the most effective at reducing usage and did so without compromising patient safety. Unnecessary computed tomography (CT) scans burden the health care system, leading to increased emergency department (ED) wait times and lengths of stay, costing almost a billion dollars annually. This study aimed to describe ED-based interventions that are most effective at reducing CT imaging while maintaining diagnostic accuracy and patient safety. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, MEDLINE, Embase, CINAHL, Cochrane Central Register of Controlled Trials, and Google Scholar were searched until December 31, 2020. Randomized and nonrandomized studies that assessed the effect of an ED-based intervention on CT scan usage were included. Abstract screening, data extraction, and quality assessment were conducted in duplicate. The Grading of Recommendation Assessment, Development and Evaluation framework, with the Risk of Bias 2 and Risk of Bias in Nonrandomized Studies - of Interventions tools, was used to determine the certainty of evidence. Significant clinical and statistical heterogeneity precluded meta-analysis; hence, a narrative synthesis was conducted. A total of 149 studies were included of 5,667 screened abstracts, with substantial interrater reliability among reviewers (Cohen’s κ>0.60). The CT reduction strategies were categorized into 15 single and 11 multimodal interventions by consensus review. Interventions that consistently reduced CT usage included diagnostic pathways, alternative test availability, specialist involvement, and provider feedback. Family/patient education, clinical decision support tools, or passive guideline dissemination did not consistently reduce usage. Only 44% of studies reported unintended consequences of reduction strategies; however, these showed no increase in missed diagnoses or patient harm. The interventions that engaged multiple specialties during planning/implementation had a greater reduction effect than ED only. The certainty of evidence for the primary outcome was very low. Multidisciplinary-led interventions that provided an alternative to CT imaging were the most effective at reducing usage and did so without compromising patient safety.
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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 | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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