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
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".