Doctors can agree: Enhancing interrater reliability of mental health diagnosis among junior psychiatrists using electronic clinician assisting technology
Bibliographic record
Abstract
Interrater reliability in mental health diagnoses presents significant challenges, hindering consistent patient care. This study investigated the dual impact of clinicians' seniority and the use of an Electronic Medical Records (EMR) integrated Clinical Decision Support System (CDSS) on the diagnostic concordance of Major Depressive Disorder (MDD), Bipolar Disorder (BD), and Schizophrenia (SCZ). Seventy-two psychiatrists were randomly assigned to an EMR-assisted or a non-assisted control group, stratified by seniority. Participants diagnosed three cases based on video-recorded structured clinical interviews, and interrater reliability was assessed using Gwet's AC1. Results showed that seniority was a critical factor, with mid-level psychiatrists demonstrating higher reliability than their low-seniority counterparts. The CDSS intervention also significantly improved concordance. This effect was driven by dramatic improvements for mood disorders. The system's impact was negligible for SCZ, which showed high baseline agreement, and among high-seniority psychiatrists, who achieved perfect concordance regardless of CDSS use. This study underscores the potential of EMR-integrated CDSS to enhance psychiatric diagnostic consistency, likely by standardizing the evaluation process for less-experienced clinicians facing complex differential diagnosis of mood disorders. These findings highlight the need to evaluate such systems in diverse clinical settings and across a broader spectrum of psychiatric conditions. • Study seniority & EMR tools' impact on MDD, BD, SCZ interrater reliability. • 72 psychiatrists assessed video interviews, split into EMR/non-EMR groups. • Mid seniority group had better diagnostic concordance than Low seniority group. • The EMR-assisted group had better diagnostic concordance for mood disorders. • EMR assistance is less helpful for SCZ diagnoses & experienced clinicians.
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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 | 0.071 | 0.279 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".