The effects of trained observers (dofficers) and audits during a facility-wide COVID-19 outbreak: A mixed-methods quality improvement analysis
Notice bibliographique
Résumé
•Personal protective equipment errors are common.•Bundled infection prevention control audits and continuous quality improvement may reduce pathogen spread.•Audits by trained observers (dofficers) can be used by outbreak response teams to identify opportunities for infection prevention and control quality improvement.•Real-time feedback from dofficers, and quality improvement feedback from an outbreak response team may be associated with a reduction in infection control errors.•Further research is needed to determine if dofficers are causally linked with error reductions. BackgroundIn response to a facility-wide COVID-19 outbreak, our tertiary acute care hospital implemented an evidence-based bundle of infection control practices including the use of audits and trained observers “dofficers” to provide real-time constructive feedback.MethodsWe trained furloughed staff to perform the role of dofficer. They offered support and corrective feedback on proper PPE use and completed 21-point audits during a 4-week intervention period. Audits tracked appropriate signage, placement and availability of supplies (equipment), correct PPE use, enhanced environmental cleaning, along with cohorting and social distancing rates. Audit data was used to provide weekly quality improvement reports to units.ResultsNine hundred and sixty two separate audits recorded 36,948 observations, over 7,696 observer-hours. The most common errors were with environmental cleaning and PPE use; the least common were with regards to equipment availability and cohorting and social distancing. Mean error rates decreased from 9.81% to 2.88% (P < .001). The largest reduction, 22.57%, occurred in the category of PPE doffing errors.ConclusionsDofficer led audits effectively identified areas for improvement. Feedback through weekly reports and real-time correction of PPE errors by dofficers led to statistically significant improvements; however, error rates remained high. Further research is needed establish if these relationships are causal. In response to a facility-wide COVID-19 outbreak, our tertiary acute care hospital implemented an evidence-based bundle of infection control practices including the use of audits and trained observers “dofficers” to provide real-time constructive feedback. We trained furloughed staff to perform the role of dofficer. They offered support and corrective feedback on proper PPE use and completed 21-point audits during a 4-week intervention period. Audits tracked appropriate signage, placement and availability of supplies (equipment), correct PPE use, enhanced environmental cleaning, along with cohorting and social distancing rates. Audit data was used to provide weekly quality improvement reports to units. Nine hundred and sixty two separate audits recorded 36,948 observations, over 7,696 observer-hours. The most common errors were with environmental cleaning and PPE use; the least common were with regards to equipment availability and cohorting and social distancing. Mean error rates decreased from 9.81% to 2.88% (P < .001). The largest reduction, 22.57%, occurred in the category of PPE doffing errors. Dofficer led audits effectively identified areas for improvement. Feedback through weekly reports and real-time correction of PPE errors by dofficers led to statistically significant improvements; however, error rates remained high. Further research is needed establish if these relationships are causal.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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 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 ».