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TRUVACE RECORD VERSION
record: TRV-2026-1993
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T06:55:49.288446Z
status: published
lens: g_space
sector: health
headline: Predicting Self-Harm Events After Emergency Department Visits Associated With Mental or Substance Use Disorder Diagnoses
dek: Objective Using a machine learning model, the authors aimed to predict self-harm events within 90 days of an emergency department (ED) visit associated with a mental or substance use disorder diagnosis. Methods Electronic health record and claims data of patients ages ≥11 years from seven integrated health systems were used to identify ED visits and diagnoses. Data on 1,254 predictors were extracted for the 60 months before an ED visit, and self-harm was measured in the 90 days after this visit. Logistic regress…
gain_title: A machine learning model using health record and claims data separated emergency department patients by 90-day self-harm risk, which could help target safety planning and follow-up after discharge.
problem_title: (none)
trace_subject: (none)
gain_reading: A machine learning model using health record and claims data separated emergency department patients by 90-day self-harm risk, which could help target safety planning and follow-up after discharge.
gain_evidence: could be useful in identifying ED patients who may need safety planning and close follow-up after discharge
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers analyzed 1,554,828 emergency department visits by 803,413 patients aged 11 and older across seven integrated health systems. Each visit was linked to a mental or substance use disorder diagnosis, and the team used 1,254 predictors drawn from the prior 60 months of records and claims to estimate self-harm in the following 90 days.

The findings suggest that routinely collected data can flag which patients leave the emergency department at greatest risk, potentially guiding limited follow-up resources. The abstract describes the tool as potentially useful rather than demonstrating that using it reduces self-harm, so its effect on patient outcomes remains untested here.
limitation: 
tag: Evidence-backed gain
key_points: Self-harm occurred after 19,603 visits, or 1.3% of the 1,554,828 visits studied. | The best-fitting model reached an area under the curve of 0.85, with performance consistent across various subgroups. | Visits at the 95th percentile of predicted risk had a 10.8% self-harm rate, versus 2.4% at the 50th percentile. | The highest risk stratum had an observed self-harm risk of 27.2%, about 136 times that of the lowest stratum.
rundown: The study drew on electronic health record and claims data from seven integrated health systems. It covered patients aged 11 and older whose emergency department visits were associated with a mental or substance use disorder diagnosis, with predictors extracted from the 60 months before each visit.

Logistic regression models estimated self-harm events, and performance was assessed using out-of-sample cross-validation predictions. Self-harm was measured in the 90 days after each visit.

The authors conclude that such models could help identify patients who may need safety planning and close follow-up after discharge.
sources:
- peer_reviewed | Psychiatric Services | https://doi.org/10.1176/appi.ps.20250697 | 2026-10-14
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