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Data-driven Identification for Substance Misuse

NCT03833804 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 64996

Last updated 2025-10-24

Study results available
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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.

Conditions

Interventions

OTHER

Processing of clinical notes in the EHR data collected during routine care

Clinical notes collected in the first day of hospital admission during usual care as input to natural language processing and machine learning algorithm.

Sponsors & Collaborators

  • Rush University Medical Center

    collaborator OTHER

  • National Institute on Drug Abuse (NIDA)

    collaborator NIH

  • University of Wisconsin, Madison

    lead OTHER

Study Design

Allocation
NA
Purpose
SCREENING
Masking
NONE
Model
SEQUENTIAL

Eligibility

Min Age
18 Years
Max Age
89 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-09-19
Primary Completion
2024-09-19
Completion
2024-09-19

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03833804 on ClinicalTrials.gov