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Machine Learning Model

Data-Driven Screening for Substance Use Disorders

N/A
Recruiting
Research Sponsored by University of Wisconsin, Madison
Eligibility Criteria Checklist
Specific guidelines that determine who can or cannot participate in a clinical trial
Must have
Ages 18 years old to 89 years old
Inpatient status during hospitalization
Timeline
Screening 3 weeks
Treatment Varies
Follow Up 12 months enrollment with 6 months follow-up for rehospitalization
Awards & highlights

Study Summary

This trial aims to create a machine learning model to scan hospital patient records & detect substance misuse, providing a standardized approach for better screening & treatment.

Who is the study for?
This trial is for hospitalized patients aged 18 to 89 who are staying longer than a day. It's not for those transferred to/from another acute hospital, unable to participate in usual care interventions, discharged against advice, or critically ill within the first day.Check my eligibility
What is being tested?
The study is testing a machine learning model that processes clinical notes from electronic health records (EHR) to identify substance misuse. The goal is an automated daily screen tool for all hospitalized patients.See study design
What are the potential side effects?
Since this trial involves data processing and not medical treatments or drugs, there are no direct physical side effects associated with participating in this research.

Eligibility Criteria

Inclusion Criteria

You may be eligible if you check “Yes” for the criteria below
Select...
I am between 18 and 89 years old.
Select...
I am currently admitted to the hospital.

Timeline

Screening ~ 3 weeks
Treatment ~ Varies
Follow Up ~12 months enrollment with 6 months follow-up for rehospitalization
This trial's timeline: 3 weeks for screening, Varies for treatment, and 12 months enrollment with 6 months follow-up for rehospitalization for reporting.

Treatment Details

Study Objectives

Outcome measures can provide a clearer picture of what you can expect from a treatment.
Primary outcome measures
Proportion of patients that had a universal screen positive and received SBIRT (screening, brief intervention, or referral to treatment)
Secondary outcome measures
All-cause re-hospitalizations following 6-months from the Index hospital encounter

Trial Design

1Treatment groups
Experimental Treatment
Group I: NLP (natural language processing) pre-screenExperimental Treatment1 Intervention
Automated processing of clinical notes collected during routine care in first 24 hours of hospital admission to identify individuals at-risk for substance misuse to receive standard-of-care full screening and assessment, brief intervention, or referral to treatment (SBIRT) intervention.

Find a Location

Who is running the clinical trial?

Rush University Medical CenterOTHER
424 Previous Clinical Trials
121,935 Total Patients Enrolled
1 Trials studying Substance Abuse
801 Patients Enrolled for Substance Abuse
University of Wisconsin, MadisonLead Sponsor
1,193 Previous Clinical Trials
3,127,139 Total Patients Enrolled
1 Trials studying Substance Abuse
53,132 Patients Enrolled for Substance Abuse

Media Library

Data-driven Identification for Substance Misuse (Machine Learning Model) Clinical Trial Eligibility Overview. Trial Name: NCT03833804 — N/A
Substance Abuse Research Study Groups: NLP (natural language processing) pre-screen
Substance Abuse Clinical Trial 2023: Data-driven Identification for Substance Misuse Highlights & Side Effects. Trial Name: NCT03833804 — N/A
Data-driven Identification for Substance Misuse (Machine Learning Model) 2023 Treatment Timeline for Medical Study. Trial Name: NCT03833804 — N/A
Substance Abuse Patient Testimony for trial: Trial Name: NCT03833804 — N/A
~10591 spots leftby Jan 2025
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