Early identification of mental health disorders: Using the mental health inventory-5 (MHI-5) in a free-standing emergency room
Bibliographic record
Abstract
Emotional distress in high-acuity emergency care settings is frequently underrecognized, resulting in missed opportunities for early behavioral health intervention. At a free-standing Emergency Room (ER) in a small, semi-urban city in a largely rural region of Texas, internal audits revealed a 100% miss rate for identifying adult patients with symptoms of depression or anxiety during triage. Professional guidelines, including recommendations from the American Association for Emergency Psychiatry and the U.S. Preventive Services Task Force, emphasize routine mental health screening in emergency departments to improve patient safety and outcomes. National data indicate increasing psychiatric-related visits, particularly among youth, many of whom remain undiagnosed. Undiagnosed emotional distress also places additional burdens on emergency care providers managing complex physical presentations without awareness of underlying psychological factors. This highlights the critical need for structured mental health screening protocols in emergency settings to enhance early detection and support comprehensive patient care. This quality improvement project explored how embedding the Mental Health Inventory-5 (MHI-5) screening tool into the intake process at a freestanding emergency department affected early detection of psychological distress in adults aged 18 and older. Over a nine-week implementation period, 710 of 2,583 eligible patients (27.5%) completed the screening. A total of 147 patients screened positive for psychological distress (20.7%); however, only 38 (25.9%) were documented in the electronic health record, reflecting a significant gap in follow-through. Staff noted increased awareness and confidence in addressing mental health concerns. The MHI-5 screening tool identified emotional needs not captured during routine triage. Recommendations include improving documentation workflows, adding EHR prompts, and continuing staff training to support consistent use.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".