Chicago Data-driven Opioid Use Disorder Screening, Engagement, Treatment and Planning System (C-DOSETaP)

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

Study Overview

• Status: Not yet recruiting
• Conditions: Opiod Use Disorder
• Intervention / Treatment: Other: OUD screening; Other: MOUD; Other: Continuity of care

Detailed Description

The Chicago Data-driven Opioid use disorder Screening, Engagement, Treatment and Planning (C-DOSETaP) System, tests an innovative approach leveraging healthcare data harmonization, digital tools, and clinical workflows to improve the care for patients with opioid use disorder (OUD) across a large health system serving a population heavily affected by the opioid overdose epidemic. The C-DOSETaP system will implement a diverse set of screening tools across the health systems' numerous clinical domains, improve healthcare engagement and utilization of OUD treatments, and pursue a data-forward approach leveraging electronic health record (EHR) data to track care delivery and engage with patients at risk for treatment dropout or failure.

The investigators hypothesize that implementation of the C-DOSETaP system alongside a locally developed system-level opioid response plan will result in: 1) increased OUD screening rates; 2) improved continuity of care; 3) increased utilization of medications for opioid use disorder (MOUD); and 4) reduced mortality in neighborhoods served by the primary study institution.

Primary Outcomes Three dimensions of OUD treatment and engagement will be assessed as primary outcomes for the study. The investigators will measure: 1) screening rates; 2) continuity of care; and 3) use of MOUD across the health system. Screening rates will be measured as the proportion of all patients with encounters in the health system that have a completed screening for opioid misuse within the preceding 12 months. Continuity of care will be assessed by appointment follow-up and completed referral to the next care site. Use of MOUD will be measured as the number of patients actively on MOUD as a proportion of all patients with documented OUD within the health system as defined by International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes.

Secondary Outcomes The investigators plan to evaluate aggregate impact of interventions and primary measures on OUD mortality reported in neighborhoods served by the primary study institution during the phased stepped wedge rollout across system-associated clinics. The secondary outcomes for this phase include quarterly opioid-related mortality by zip codes served by the primary institution.

• Study Type: Interventional
• Enrollment (Estimated): 271031
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Niranjan S. Karnik, MD, PhD; Phone Number: (312) 273-0185; Email: nkarnik@uic.edu
• Study Contact Backup: Name: Neeraj Chhabra, MD; Email: neeraj1@uic.edu

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60608
      • University of Illinois Hospitals and Clinics (UI Health)
      • Contact: Neeraj Chhabra, MD; Email: neeraj1@uic.edu
      • Contact: Niranjan S. Karnik, MD, PhD; Phone Number: 312-273-0185; Email: nkarnik@uic.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria

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

Exclusion Criteria

- Children younger than age 16

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Patients with OUD; Patients with opioid use disorder identified through C-DOSETaP system	Other: OUD screening; Completed screening for opioid use disorder; Other: MOUD; Medication treatment for opioid use disorder; Other: Continuity of care; Facilitation of outpatient treatment linkages

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Regional opioid-related mortality	Quarterly opioid-related mortality rate by zip codes served by the primary institution reported to the Illinois Department of Public Health	Baseline and 12 months
Regional OUD Screening	Quarterly opioid-related screening rates by zip codes served by the primary institution reported to the Illinois Department of Public Health	Baseline and 12 months
Regional MOUD utilization	Quarterly MOUD utilization rates by zip codes served by the primary institution reported to the Illinois Department of Public Health	Baseline and 12 months

Collaborators and Investigators

• Sponsor: University of Illinois at Chicago
• Collaborators: National Institute on Drug Abuse (NIDA)
• Investigators: Principal Investigator: Niranjan S. Karnik, MD, PhD, UIC, College of Medicine; Principal Investigator: Neeraj Chhabra, MD, UIC, College of Medicine

Publications and helpful links

General Publications

• 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.
• 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.
• 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.

Helpful Links

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

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): June 30, 2029

Study Registration Dates

• First Submitted: March 13, 2026
• First Submitted That Met QC Criteria: March 23, 2026
• First Posted (Actual): March 27, 2026

Study Record Updates

• Last Update Posted (Actual): April 1, 2026
• Last Update Submitted That Met QC Criteria: March 26, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Opioid Use Disorder (OUD); Buprenorphine; Artificial Intelligence (AI); opioid misuse; health systems; Machine learning (ML); Medications for opioid use disorder (MOUD); Natural language processing (NLP)
• Additional Relevant MeSH Terms: Narcotic-Related Disorders; Mental Disorders; Substance-Related Disorders; Chemically-Induced Disorders; Opioid-Related Disorders; Health Services Administration; Therapeutics; Patient Care; Health Services; Health Care Facilities Workforce and Services; Patient Care Management; Comprehensive Health Care; Primary Health Care; Continuity of Patient Care
• Other Study ID Numbers: 2024-0243; 1R61DA057629 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: A de-identified research dataset will be submitted to NIH data repository per the approved Data Management and Sharing Plan following R33 completion.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No