The Implementation of a Virtual Emergency Department: Multimethods Study Guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) Framework
Notice bibliographique
Résumé
BACKGROUND: While the COVID-19 pandemic dramatically increased virtual care uptake across many health settings, it remains significantly underused in urgent care. OBJECTIVE: This study evaluated the implementation of a pilot virtual emergency department (VED) at an Ontario hospital that connected patients to emergency physicians through a web-based portal. We sought to (1) assess the acceptability of the VED model, (2) evaluate whether the VED was implemented as intended, and (3) explore the impact on quality of care, access to care, and continuity of care. METHODS: This evaluation used a multimethods approach informed by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Data included semistructured interviews with patients and physicians as well as postvisit surveys from patients. Interviews were transcribed and analyzed using thematic analysis. Data from the surveys were described using summary statistics. RESULTS: From December 2020 to December 2021, the VED had a mean of 153 (SD 25) visits per month. Among them, 67% (n=677) were female, and 75% (n=758) had a family physician. Patients reported that the VED provided high-quality, timely access to care and praised the convenience, shorter appointments, and benefit of the calm, safe space afforded through virtual appointments. In instances where patients were directed to come into the emergency department (ED), physicians were able to provide a "warm handoff" to improve efficiency. This helped manage patient expectations, and the direct advice of the ED physician reassured them that the visit was warranted. There was broad initial uptake of VED shifts among ED physicians with 60% (n=22) completing shifts in the first 2 months and 42% (n=15) completing 1 or more shifts per month over the course of the pilot. There were no difficulties finding sufficient ED physicians for shifts. Most physicians enjoyed working in the VED, saw value for patients, and were motivated by patient satisfaction. However, some physicians were hesitant as they felt their expertise and skills as ED physicians were underused. The VED was implemented using an iterative staged approach with increased service capabilities over time, including access to ultrasounds, virtual follow-ups after a recent ED visit, and access to blood work, urine tests, and x-rays (at the hospital or a local community laboratory). Physicians recognized the value in supporting patients by advising on the need for an in-person visit, booking a diagnostic test, or referring them to a specialist. CONCLUSIONS: The VED had the support of physicians and facilitated care for low-acuity presentations with immediate benefits for patients. It has the potential to benefit the health care system by seeing patients through the web and guiding patients to in-person care only when necessary. Long-term sustainability requires a focus on understanding digital equity and enhanced access to rapid testing or investigations.
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 | 0,044 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».