Opiod Use Disorder
Conditions
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
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. 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.
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.
Interventions
OTHEROUD screening
Completed screening for opioid use disorder
OTHERMOUD
Medication treatment for opioid use disorder
OTHERContinuity of care
Facilitation of outpatient treatment linkages
Sponsors
University of Illinois at Chicago
National Institute on Drug Abuse (NIDA)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
16 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of people screened for OUD | Rolling measure of annual rates (12 months) measured over the implementation period. | Aggregate rate of data-driven OUD screening across the health system |
| Continuity of care for patients with OUD | 30 days and 12 months | Continuity of care will be assessed through appointment follow-up and completion of referral to the next care site within 30 days. |
| Utilization of MOUD across the health system | Baseline and 12 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Regional opioid-related mortality | Baseline and 12 months | Quarterly opioid-related mortality rate by zip codes served by the primary institution reported to the Illinois Department of Public Health |
| Regional OUD Screening | Baseline and 12 months | Quarterly opioid-related screening rates by zip codes served by the primary institution reported to the Illinois Department of Public Health |
| Regional MOUD utilization | Baseline and 12 months | Quarterly MOUD utilization rates by zip codes served by the primary institution reported to the Illinois Department of Public Health |
Countries
United States
Contacts
CONTACTNiranjan S. Karnik, MD, PhD
CONTACTNeeraj Chhabra, MD
PRINCIPAL_INVESTIGATORNiranjan S. Karnik, MD, PhD
UIC, College of Medicine
PRINCIPAL_INVESTIGATORNeeraj Chhabra, MD
UIC, College of Medicine
Outcome results
None listed