Chicago Data-driven Opioid Use Disorder Screening, Engagement, Treatment and Planning System

NCT07498322 | Not Yet Recruiting

• 2 other identifiers: 2024-02431R61DA057629
• Study Type: interventional
• Target: 271,031
• Locations: 1 country
• Sites: 1

Brief Summary

This study, called the Chicago Data-driven Opioid use disorder Screening, Engagement, Treatment and Planning (C-DOSETaP) System, tests a new system of clinical care for patients with opioid use disorder (OUD) across a large health system. The main questions this study aims to answer are:

1. 1.Does the C-DOSETaP System increase screening for patients with OUD;
2. 2.Does the C-DOSETaP System improve continuity of health care for patients with OUD;
3. 3.Does the C-DOSETaP System increase use of medications for opioid use disorder; and
4. 4.Does the C-DOSETaP System reduce the number of opioid-related deaths in the neighborhoods served.

Conditions

Opiod Use Disorder

Keywords

Opioid Use Disorder (OUD); Natural language processing (NLP); Machine learning (ML); Medications for opioid use disorder (MOUD); health systems; Artificial Intelligence (AI); opioid misuse; Buprenorphine

Outcome Measures

Primary Outcomes (3)

• Number of people screened for OUD

  Aggregate rate of data-driven OUD screening across the health system

  Rolling measure of annual rates (12 months) measured over the implementation period.

• Continuity of care for patients with OUD

  Continuity of care will be assessed through appointment follow-up and completion of referral to the next care site within 30 days.

  30 days and 12 months

• Utilization of MOUD across the health system

  MOUD use will be measured as the number of patients actively on MOUD as a proportion of all patients within the health system and of those with documented OUD within the health system as defined by International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes.

  Baseline and 12 months

Secondary Outcomes (3)

• Regional opioid-related mortality

  Baseline and 12 months

• Regional OUD Screening

  Baseline and 12 months

• Regional MOUD utilization

  Baseline and 12 months

Study Arms (1)

Patients with OUD

EXPERIMENTAL

Patients with opioid use disorder identified through C-DOSETaP system

Other: OUD screening; Other: MOUD; Other: Continuity of care

Interventions

OUD screening OTHER

Completed screening for opioid use disorder

Patients with OUD

MOUD OTHER

Medication treatment for opioid use disorder

Patients with OUD

Continuity of care OTHER

Facilitation of outpatient treatment linkages

Patients with OUD

Eligibility Criteria

• Age: 16 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Participant must be a patient seen at the University of Illinois Hospital and Clinics
• Adults and adolescents age 16 or older

You may not qualify if:

• \- Children younger than age 16

Sponsors & Collaborators

• University of Illinois at Chicago: lead
• National Institute on Drug Abuse (NIDA): collaborator

Study Sites (1)

University of Illinois Hospitals and Clinics (UI Health)

Chicago, Illinois, 60608, United States

Location

Related Publications (3)

• Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, Thompson HM, Markossian T, Joyce C, Churpek MM, Karnik NS. Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. Lancet Digit Health. 2022 Jun;4(6):e426-e435. doi: 10.1016/S2589-7500(22)00041-3.

  PMID: 35623797 BACKGROUND

• Shahid U, Parde N, Smith DL, Dickinson G, Bianco J, Thorpe D, Hota M, Afshar M, Karnik NS, Chhabra N. Development and Evaluation of Machine Learning Models for the Detection of Emergency Department Patients with Opioid Misuse from Clinical Notes. medRxiv [Preprint]. 2024 Dec 12:2024.12.11.24318875. doi: 10.1101/2024.12.11.24318875.

  PMID: 39711725 BACKGROUND

• Chhablani C, Shahid U, Parde N, Muslmani S, Hu H, Thorpe D, Afshar M, Karnik N, Chhabra N. Machine learning models to detect opioid misuse in emergency department patients at triage. Am J Emerg Med. 2026 Feb 26;104:17-23. doi: 10.1016/j.ajem.2026.02.037. Online ahead of print.

  PMID: 41785519 BACKGROUND

Related Links

• The AI.Health4All Center, University of Illinois at Chicago

MeSH Terms

Conditions

Opioid-Related Disorders

Interventions

Continuity of Patient Care

Condition Hierarchy (Ancestors)

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

Intervention Hierarchy (Ancestors)

Patient Care; Therapeutics; Health Services; Health Care Facilities Workforce and Services; Primary Health Care; Comprehensive Health Care; Patient Care Management; Health Services Administration

Study Officials

• Niranjan S. Karnik, MD, PhD

  UIC, College of Medicine

  PRINCIPAL INVESTIGATOR

• Neeraj Chhabra, MD

  UIC, College of Medicine

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Niranjan S. Karnik, MD, PhD

CONTACT

(312) 273-0185; nkarnik@uic.edu

Neeraj Chhabra, MD

CONTACT

neeraj1@uic.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: The C-DOSETaP system represents a health system-level approach to OUD screening, treatment engagement, and patient retention, which comprises multiple clinical contexts, including inpatient hospital-based care, emergency care, primary care, and specialist outpatient care. Screening activities across these domains will take advantage of digital AI classifiers using EHR data in the inpatient and emergency settings and self-report measures in the outpatient clinics. Results from the opioid misuse screeners will be integrated into local clinical workflows and data aggregated to a digital dashboard for use by the health system's OUD Command Center (OCC) to track referrals for OUD care, follow-up care, and prescriptions. Automated flags will alert the OCC for signs potentially indicative of patient dropout from OUD treatment including missed appointments by more than 48 hours, missed prescriptions, and and lack of any follow-up data. Flags will trigger outreach by peer-recovery specialists.

Study Record Dates

• First Submitted: March 13, 2026
• First Posted: March 27, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): June 30, 2029
• Last Updated: April 1, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)