A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults
Bibliographic record
Abstract
Abstract The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in the treatment of adults with major depression. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. For both physicians and patients, study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust in the CDSS. 17 patients consented to participate in the study, of which 14 completed. 86% of physicians (6/7) felt the information provided by the CDSS provided a more comprehensive understanding of patient situations and 71% (5/7) felt the information was helpful. 86% of physicians (6/7) reported the AI/predictive model was useful when making treatment decisions. 62% of patients (8/13) reported improvement in their care as a result of the tool. 46% of patients (6/13) felt the app significantly or somewhat improved their relationship with their physicians; 54% felt it did not change. 71% of physicians (5/7) and 62% of patients (8/13) rated they trusted the tool. Qualitative results are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while making treatment decisions. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".