Improving the Usability of Written Exposure Therapy for Therapists in the Department of Veterans Affairs Telemental Health: Formative Study Using Qualitative and User-Centered Design Methods
Notice bibliographique
Résumé
BACKGROUND: User modifications are common in evidence-based psychosocial interventions (EBPIs) for mental health disorders. Often, EBPIs fit poorly into clinical workflows, require extensive resources, or pose considerable burden to patients and therapists. Implementation science is increasingly researching ways to improve the usability of EBPIs before implementation. A user-centered design can be used to support implementation methods to prioritize user needs and solutions to improve EBPI usability. OBJECTIVE: Trauma-focused EBPIs are a first-line treatment for patients with posttraumatic stress disorder (PTSD) in the Department of Veterans Affairs. Written exposure therapy (WET) is a brief, trauma-focused EBPI wherein patients handwrite about trauma associated with their PTSD. Initially developed for in-person delivery, WET is increasingly being delivered remotely, and outcomes appear to be equivalent to in-person delivery. However, there are logistical issues in delivering WET via video. In this evaluation, we explored usability issues related to WET telehealth delivery via videoconferencing software and designed a solution for therapist-facing challenges to systematize WET telehealth delivery. METHODS: The Discover, Design and Build, and Test framework guided this formative evaluation and served to inform a larger Virtual Care Quality Enhancement Research Initiative. We used qualitative descriptive methods in the Discover phase to understand the experiences and needs of 2 groups of users providing care within the Department of Veterans Affairs: in-person therapists delivering WET via video because of the COVID-19 pandemic and telehealth therapists who regularly deliver PTSD therapies. We then used user-centered design methods in the Design and Build phase to brainstorm, develop, and iteratively refine potential workflows to address identified usability issues. All procedures were conducted remotely. RESULTS: In the Discover phase, both groups had challenges delivering WET and other PTSD therapies via telehealth because of technology issues with videoconferencing software, environmental distractions, and workflow disruptions. Narrative transfer (ie, patients sending handwritten trauma accounts to therapists) was the first target for design solution development as it was deemed most critical to WET delivery. In the Design and Build phase, we identified design constraints and brainstormed solution ideas. This led to the development of 3 solution workflows that were presented to a subgroup of therapist users through cognitive walkthroughs. Meetings with this subgroup allowed workflow refinement to improve narrative transfers. Finally, to facilitate using these workflows, we developed PDF manuals that are being refined in subsequent phases of the implementation project (not mentioned in this paper). CONCLUSIONS: The Discover, Design and Build, and Test framework can be a useful tool for understanding user needs in complex EBPI interventions and designing solutions to user-identified usability issues. Building on this work, an iterative evaluation of the 3 solution workflows and accompanying manuals with therapists and patients is underway as part of a nationwide WET implementation in telehealth settings.
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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 | 0,073 | 0,093 |
| 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 | 0,005 | 0,007 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».