N/A
Completed N=31,432
Data-driven Identification for Substance Misuse
Substance Use Disorder · Substance Abuse · Substance-Related Disorders
Source: ClinicalTrials.gov NCT03833804 ↗
Enrolled (actual)
31,432
Serious AEs
—
Results posted
Oct 2025
Primary outcomePrimary: Proportion of Patients That Had a Universal Screen Positive and Received SBIRT (Screening, Brief Intervention, or Referral to Treatment) — 1189; 1144 Participants — p=0.20
Summary
The investigators propose to develop an open-source, publicly available machine learning model that health systems could download and apply to their electronic health record data marts to screen for substance misuse in their patients. The investigators hypothesize that the natural language processing algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for screening, brief intervention, and referral to treatment.
Outcome Measures
| Outcome | Result | p-value |
|---|---|---|
| PRIMARY Proportion of Patients That Had a Universal Screen Positive and Received SBIRT (Screening, Brief Intervention, or Referral to Treatment) |
1189; 1144 | 0.20 |
| SECONDARY All-cause Re-hospitalizations Following 6-months From the Index Hospital Encounter |
9584; 10241 | — |
Eligibility Criteria
Inclusion Criteria
- Ages 18 years old to 89 years old
- Inpatient status during hospitalization
- Length of stay greater than 24 hours
Exclusion Criteria
- Cannot participate in the usual care SBIRT intervention
- Death or obtunded during first 24 hours of admission
- Discharged against medical advice
- Transferred from another acute care hospital
- Transferred to another acute care hospital
Data sourced from ClinicalTrials.gov (NCT03833804). Outcome figures and adverse-event rates are extracted automatically from the registry's posted results and are provided for clinician reference, not as a substitute for the primary publication. Informational only — not medical advice.