Implementing Infection Control and Quality of Life Best Practices in Nursing Homes With Project ECHO: Protocol for a Patient-Centered Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Nursing homes in the United States were devastated by COVID-19, with 710,000 cases and 138,000 deaths nationally through October 2021. Although facilities are required to have infection control staff, only 3% of designated infection preventionists have taken a basic infection control course prior to the COVID-19 pandemic. Most research has focused on infection control in the acute care setting. However, little is known about the implementation of infection control practices and effective interventions in nursing homes. This study utilizes Project ECHO (Extension for Community Health Outcomes), an evidence-based telementoring model, to connect Penn State University subject matter experts with nursing home staff and administrators to proactively support evidence-based infection control guideline implementation. OBJECTIVE: Our study seeks to answer the research question of how evidence-based infection control guidelines can be implemented effectively in nursing homes, including comparing the effectiveness of two ECHO-delivered training interventions on key patient-centered outcomes such as reducing the number of residents with a COVID-19 diagnosis. METHODS: A stratified cluster randomized design was utilized. Using a 1:1 ratio, we randomly assigned 136 nursing homes to ECHO or ECHO Plus arms. Randomization was stratified by geographic location, baseline COVID-19 infection rate, and facility capacity. The study had two phases. In phase one, completed in July 2021, nursing homes in both study arms received a 16-week infectious disease and quality improvement training intervention via real-time, interactive videoconferencing and the ECHO learning model. Phase one sessions were up to 90 minutes in duration. In phase two, completed in November 2021, the ECHO group was offered optional 60-minute office hours for 9 weeks and the ECHO Plus group received 9 weeks of 60-minute sessions on emerging topics and an additional 8-session refresher series on infection control. RESULTS: A total of 290 nursing home facilities were assessed for eligibility, with 136 nursing homes recruited and randomly assigned to ECHO or ECHO Plus. Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we will simultaneously evaluate the study's effectiveness and implementation outcomes at baseline (intervention start date), and at 4, 6, 12, and 18 months. The primary outcome is the COVID-19 infection rate in nursing homes. Secondary outcomes include COVID-19 hospitalizations and deaths, flu-like illness, and quality of life. Surveys and interviews with participants will also provide data as to the adoption, implementation, and maintenance of best practices taught throughout ECHO sessions. CONCLUSIONS: A multipronged approach to improving infection control and emergency preparedness in nursing homes is important, given the toll that the COVID-19 pandemic has taken on residents and staff. The ECHO model has significant strengths when compared to traditional training, as it allows for remote learning delivered by a multidisciplinary team of experts, and utilizes case discussions that match the context and capacity of nursing homes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04499391; https://clinicaltrials.gov/ct2/show/NCT04499391.
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 | 0,049 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,003 |
| Méta-épidémiologie (sens large) | 0,014 | 0,007 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,058 | 0,009 |
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 ».