Data-driven Identification for Substance Misuse

NCT03833804 | Completed Results DMC

• 4 other identifiers: 2022-0983A534285SMPH/MEDICINE1R01DA051464
• Study Type: interventional
• Target: 64,996
• Locations: 1 country
• Sites: 1

Brief 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

Substance Use; Substance Abuse; Substance-Related Disorders

Keywords

natural language processing; machine learning; artificial intelligence; clinical decision support; unhealthy alcohol use; opioid use disorder; illicit drug use

Outcome Measures

Primary Outcomes (1)

• Proportion of Patients That Had a Universal Screen Positive and Received SBIRT (Screening, Brief Intervention, or Referral to Treatment)

  The primary outcome is the proportion of patients who received SBIRT after a positive universal screen for being at risk for substance misuse. The design is an interrupted time-series prospective observational study.

  24 months

Secondary Outcomes (1)

• All-cause Re-hospitalizations Following 6-months From the Index Hospital Encounter

  12 months enrollment with 6 months follow-up for rehospitalization

Study Arms (2)

SMART-AI: NLP (natural language processing) pre-screen

EXPERIMENTAL

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.

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

Usual Care

NO INTERVENTION

Data collected before the intervention began

Interventions

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

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

SMART-AI: NLP (natural language processing) pre-screen

Eligibility Criteria

• Age: 18 Years - 89 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Ages 18 years old to 89 years old
• Inpatient status during hospitalization
• Length of stay greater than 24 hours

You may not qualify if:

• 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

Sponsors & Collaborators

• University of Wisconsin, Madison: lead
• Rush University Medical Center: collaborator
• National Institute on Drug Abuse (NIDA): collaborator

Study Sites (1)

Rush University Medical Center

Chicago, Illinois, 60612, United States

Location

Related Publications (3)

• Afshar M, Phillips A, Karnik N, Mueller J, To D, Gonzalez R, Price R, Cooper R, Joyce C, Dligach D. Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation. J Am Med Inform Assoc. 2019 Mar 1;26(3):254-261. doi: 10.1093/jamia/ocy166.

  PMID: 30602031 RESULT

• Rojas JC, Joyce C, Markossian TW, Chaudhari V, McClintic MR, Castro F, Fairgrieve AJ, Dligach D, Oguss MK, Churpek MM, Nikolaides J, Afshar M. Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults. medRxiv [Preprint]. 2025 Nov 19:2025.11.17.25340323. doi: 10.1101/2025.11.17.25340323.

  PMID: 41332823 DERIVED

• Joyce C, Markossian TW, Nikolaides J, Ramsey E, Thompson HM, Rojas JC, Sharma B, Dligach D, Oguss MK, Cooper RS, Afshar M. The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design. JMIR Res Protoc. 2022 Dec 19;11(12):e42971. doi: 10.2196/42971.

  PMID: 36534461 DERIVED

Related Links

• login page but full code not finalized for publishing

MeSH Terms

Conditions

Substance-Related Disorders; Opioid-Related Disorders

Condition Hierarchy (Ancestors)

Chemically-Induced Disorders; Mental Disorders; Narcotic-Related Disorders

Results Point of Contact

• Title: Majid Afshar, MD
• Organization: UW School of Medicine and Public Health

mafshar@medicine.wisc.edu

608-263-1792

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Masking Details: No masking as the manual screen is already part of usual care and the automated screen will become usual care in the post-period of the pre-post design.
• Purpose: SCREENING
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Quasi-experimental design as an interrupted time series

Study Record Dates

• First Submitted: February 6, 2019
• First Posted: February 7, 2019
• Study Start: September 19, 2022
• Primary Completion: September 19, 2024
• Study Completion: September 19, 2024
• Last Updated: October 24, 2025
• Results First Posted: October 24, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: ANALYTIC CODE
• Time Frame: 12 months after completion of study and available for at least five years on github.com

Locations

US (1)