Assessment of a Digital Platform for Routine Outcome Monitoring in Psychotherapy: Usability Study and Thematic Analysis
Bibliographic record
Abstract
Background: The integration of digital tools into psychotherapy has gained increasing attention, particularly for practices such as routine outcome monitoring (ROM), which involves the regular collection of patient-reported data to inform treatment decisions. However, despite the potential benefits, the adoption of digital platforms remains limited, partly due to usability concerns and workflow misalignment. Objective: This study aimed to assess the usability of a digital platform, Mindy, designed to support psychotherapists in implementing ROM and to explore broader challenges associated with the integration of digital tools into psychotherapeutic practice. Methods: This study adopted a qualitative, 2-stage approach. Sixteen psychotherapists participated in semistructured interviews, which included task-based usability testing and reflective discussions. Participants interacted with Mindy by performing typical clinical tasks, such as creating patient profiles, managing session data, and sending questionnaires. The first stage of analysis used a deductive thematic approach focused on predefined platform functionalities. The second stage followed an inductive methodology to identify broader themes related to the integration of digital tools in psychotherapy. Results: The usability assessment identified strengths in the platform's appointment scheduling, questionnaire delivery, and dashboard functionalities, which were perceived as intuitive and supportive of ROM practices. However, limitations were reported in areas such as documentation flexibility, interoperability with other systems, and control over information sharing with patients. Broader thematic analysis revealed three main challenges: (1) the tension between standardized documentation and the need for narrative and implicit information; (2) difficulties in embedding digital platforms into existing therapeutic workflows, especially for clinicians less familiar with technology; and (3) concerns about confidentiality and the potential for misinterpretation when sharing therapeutic notes with patients. Conclusions: These findings underscore the importance of considering both technical and contextual dimensions when developing and implementing digital platforms in mental health care. Tailoring digital tools to the needs and practices of psychotherapists may improve adoption and ultimately enhance the quality of care.
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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 | 0.111 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".