Assumptions, Perceptions, and Experiences of Behavioral Health Providers Using Telemedicine: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: The urgent and reactive implementation of telemedicine during the pandemic does not represent a long-term, strategic, and proactive approach to optimizing this technology. The assumptions, perceptions, and experiences of the behavioral health providers using telemedicine can inform system-wide and institutional-level strategies to promote longitudinal maintenance of care delivery, which can reduce the use of high-cost care due to new symptom onset and symptom exacerbation related to service interruptions. OBJECTIVE: We aim to identify the assumptions, perspectives, and experiences of behavioral health clinicians and providers using telemedicine to inform the development of an optimized, sustainable approach to telemedicine implementation. METHODS: This qualitative study applies the domains of the Consolidated Framework for Implementation Research (CFIR) to structure data collection and analysis from behavioral health providers using telemedicine via an audiovisual connection in the New England region. In total, 12 providers across levels of care were recruited for a 60-minute interview, developed from the CFIR interview guide. Atlas Ti Qualitative Software (version 23; ATLAS.ti Scientific Software Development GmbH) was used to coordinate and facilitate coding among 3 reviewers. Deductive coding was provided from the CFIR interview guide, allowing for data to be categorized by domain and construct. Constructs were analyzed for descriptive themes and tabulated for response frequency. Uncoded data were reviewed and coded in vivo to explore variables contributing to participant perceptions of experience with telemedicine use. Descriptive themes, then analytical themes, were identified. Analytical themes and tabulated frequency of response data were summarized. Finally, a sentiment analysis was completed to derive tone and meaning from the data. RESULTS: Results are reported within the CFIR domains: intervention characteristic, outer setting, inner setting, characteristics of individuals, and process. The findings with ≥90% agreement include "best practice standards were not known"; "telemedicine was believed to be efficient and time-saving for the patient and provider, maximizing productivity and thus increasing access to care"; "telemedicine provided an additional option for patients to access services, promoting sustained continuity and timeliness of care"; "participants did not identify any clear goals related to telemedicine use"; "demonstrated positive affective responses to telemedicine use"; "expressed high efficacy with telemedicine utilization"; and "strong leadership support." CONCLUSIONS: These findings support the development of interstate compacts advancing licensure across state lines; payment parity across modalities of care to ensure the financial vitality of behavioral health services; improved dissemination of telehealth training and resources, and telehealth training in academic programs of the health professions; seamless, dynamic workflows to accommodate the changing needs of patient and care continuity; emergency response protocols; and community partnerships to provide private spaces needed for a therapeutic encounter. Future research exploring the patient's experience with telemedicine is needed for all stakeholders to be represented in developing a sustainable, integrated system.
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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 | 0,021 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».