Implementation and Evaluation of a Virtual Transitional Care Intervention Using Automated Text Messaging and Virtual Visits After Emergency Department Discharges: Retrospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Emergency Department (ED) overcrowding and avoidable revisits represent significant challenges for healthcare systems, with approximately 20% of patients returning to the ED within 30 days of discharge. To reduce avoidable acute care use, many health systems have adopted ED-based transitional care interventions (TCIs). Among the most scalable and cost-effective strategies is automated text messaging outreach, which facilitates timely follow-up and reinforces discharge instructions. Despite its promise, evidence supporting this approach remains limited. OBJECTIVE: (1) Describe the design, implementation, and outcomes of a novel TCI utilizing SMS text messaging and virtual transitional care visits, and (2) assess its effect on unplanned ED revisits for the same presenting complaint as well as subsequent ambulatory follow-up engagement. METHODS: This retrospective observational cohort study included patients discharged from four EDs within a single U.S. health system between September 2023 and September 2024. Patients were categorized into two groups based on their engagement with the intervention: (1) the Completed Virtual Transitional Care Visit group (requested, scheduled, and completed a visit) and the (2) Noncompleted Virtual Transitional Care Visit group (requested, scheduled, but did not complete a visit). The primary outcome was spontaneous, unplanned ED revisits within 90 days. Secondary outcomes included outpatient follow-up and time to first outpatient evaluation. Between group differences were assessed using descriptive statistics and multivariable regression models (P < 0.05). RESULTS: Of the 68,115 discharged patients during the study period, 42.7% (29,100) received an automated text for the virtual transitional care program, and 2.9% (853/29,100) accessed the scheduling link. Of these, 56.5% (482/853) requested a virtual transitional care visit, 49.8% (240/482) scheduled an appointment, and 70.0% (168/240) completed the visit (Completed group). Among the 72 Noncompleted patients, 56.9% no-showed, 31.9% canceled, and 11.1% scheduled two appointments but completed neither. Nearly half (48.6%) of the Noncompleted group had an outpatient follow-up, indicating variable engagement. Demographics, comorbidities, and clinical acuity were similar between groups. The Noncompleted group was nearly twice as likely to return to the ED within 90 days (27.8% vs 15.5%; χ²₁=4.20, P=0.04; OR=2.11, 95% CI 1.02-4.33) while the Completed group was more likely to complete outpatient follow-up (48.6% vs 30.0%; χ²₁=6.60, P=0.01; OR=2.17, 95% CI 1.23-3.83). Time to first outpatient visit did not differ significantly between groups (mean = 15.7 days vs. 19.8 days; Δβ = -1.93; 95% CI: -10.09 to 6.42; P = 0.65). CONCLUSIONS: A TCI combining automated text messaging with virtual visits was associated with reduced 90-day spontaneous ED revisits and increased outpatient follow-up. While the intervention demonstrated significant clinical benefits among engaged patients, the low initial engagement rate (2.9%) highlights substantial challenges in achieving population-level impact. Future efforts should focus on optimizing care delivery for engaged patients while developing strategies to expand program reach across the broader ED discharge population.
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 | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».