Text Messaging Interventions for Unhealthy Alcohol Use in Emergency Departments: Mixed Methods Assessment of Implementation Barriers and Facilitators
Notice bibliographique
Résumé
Background: Many patients with unhealthy alcohol use (UAU) access health care in emergency departments (EDs). Scalable supports, such as SMS text messaging interventions, are acceptable and feasible to enhance care delivery for many health issues, including substance use. Further, SMS text messaging interventions have been shown to improve patient outcomes related to alcohol consumption (eg, reduced consumption compared to no intervention, basic health information, or drink tracking), but they are rarely offered in clinical settings. Objective: This paper describes a mixed methods study using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework. The goal of this study was to use a stakeholder-engaged mixed methods design to assess barriers and facilitators to the implementation of SMS text messaging interventions for UAU in EDs with a focus on the recipient's characteristics, the innovation's degree of fit within the existing practice, and the unique nature of the inner and outer context. Methods: This study was conducted in a large health system in the northeastern United States. We examined electronic health record data on alcohol screening in 17 EDs; surveyed 26 ED physician chairpersons on implementation feasibility, acceptability, and appropriateness; and interviewed 18 ED staff and 21 patients to understand barriers and facilitators to implementation. Interviews were analyzed according to the i-PARIHS framework to assess recipient characteristics, innovation degree of fit, and inner and outer context. Results: Electronic health record data revealed high variability in alcohol screening completion (mean 73%, range 35%-93%), indicating potential issues in identifying patients eligible to offer the intervention. The 26 ED chair surveys revealed a relatively high level of implementation confidence (mean 4, SD 0.81), acceptability (mean 4, SD 0.71), and appropriateness (mean 3.75, SD 0.69) regarding the UAU SMS text messaging intervention; feasibility (mean 3.5, SD 0.55) had the lowest mean, indicating concerns about integrating the text intervention in the busy ED workflow. Staff were concerned about staff buy-in and adding additional discussion points to already overwhelmed patients during their ED visit but saw the need for additional low-threshold services for UAU. Patients were interested in the intervention to address drinking and health-related goals. Conclusions: ED visits involving UAU have increased in the United States. The results of this formative study on barriers and facilitators to the implementation of UAU SMS text messaging interventions in EDs indicate both promise and caution. In general, we found that staff viewed offering such interventions as appropriate and acceptable; however, there were concerns with feasibility (eg, low alcohol risk screening rates). Patients also generally viewed the SMS text messaging intervention positively, with limited drawbacks (eg, slight concerns about having time to read messages). The results provide information that can be used to develop implementation strategies that can be tested in future studies.
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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 | 0,057 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».