Perceptions and insights: A qualitative assessment of an AI-assisted psychiatric triage system implemented in an outpatient hospital setting
Bibliographic record
Abstract
Introduction: The Canadian healthcare system is approaching a breaking point. With mental health being a leading cause of disability, innovative solutions are necessary to provide adequate care. Digital mental health programs, such as electronic cognitive behavioral therapy, have proven effective in reducing the challenges of traditional psychotherapy, such as long waitlists, stigma, geographic barriers, and time constraints. Furthermore, artificial intelligence (AI) has shown potential utility within the healthcare system, particularly in treatment recommendations and improving patient engagement. Despite the benefits that AI and digital mental health programs provide, they are rarely implemented in real-world healthcare settings. Objective: This study aims to explore patient experiences and perceptions of an AI-assisted triage system paired with a digital psychotherapy program. The objective is to highlight the potential modifiable barriers to implementing these digital systems in real-world healthcare settings. Methods: = 45) who used an AI-assisted triaging system and digital psychotherapy modules were surveyed through Qualtrics. This survey examined their perceptions of AI within mental healthcare, its utility within triaging, and their experiences with the digital psychotherapy program. Free-text survey responses were independently coded and analyzed using thematic analysis. Results: Thematic analysis revealed three major themes for clients' perceptions of the AI-assisted triage system: (1) AI as a replacement, (2) the utility of AI, and (3) AI complexity recognition. For the digital psychotherapy program, the themes were: (1) interactions with technology, (2) online therapy program structure, and (3) differential user experience. Conclusion: Participants highlighted the importance of human oversight to ensure accuracy and liked that the AI allowed them to access care faster. Suggestions for improving the digital psychotherapy program included enhancing user-friendliness, increasing human contact, and making it more accessible for neurodivergent individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".