Natural Language Processing for Screening Opioid Misuse

NCT05745480 | Completed

• 2 other identifiers: 2022-0384Pulmonary Medicine
• Study Type: observational
• Target: 47,502
• Locations: 1 country
• Sites: 1

Brief Summary

This is a clinical study to implement and evaluate a hospital-wide, operational intervention for a real-time natural language processing (NLP)-driven clinical decision support (CDS) tool, called Substance Misuse Algorithm for Referral to Treatment Using Artificial Intelligence (SMART-AI). The SMART-AI CDS tool will be evaluated via implementation in the UW Health electronic health record (EHR). The CDS tool is meant for screening inpatient adults for opioid misuse as part of a best practice alert to nurses and providers for addiction consult service needs.

Conditions

Opioid Use Disorder; Opioid Misuse

Keywords

Natural Language Processing (NLP); electronic health record (EHR); clinical decision support (CDS); Quality Improvement

Outcome Measures

Primary Outcomes (1)

• Percentage of inpatients who screened positive (or would have screened positive) based on the NLP CDS tool who received an addiction consult

  Percentage of inpatients who screened positive (or would have screened positive) based on the NLP CDS tool who received an addiction consult with any of the following interventions: (1) receipt of opioid use intervention or motivational interviewing (MI); (2) receipt of medication-assisted treatment (MAT); and/or (3) referral to substance use disorder treatment.

  Up to 6 months

Secondary Outcomes (1)

• 30-day unplanned hospital readmission rate

  Up to 6 months

Study Arms (2)

Pre-Intervention Period: Usual Care with Ad-Hoc Addiction Consults

UW Hospital launched an Addiction Medicine inpatient consult service in 1991 to address the high prevalence of substance use disorders in hospitalized adults. Currently, a single screening item queries 'marijuana or other recreational drug use,' but no formal screening process was in place specifically targeting opioid misuse. For patients at risk of an opioid use disorder, the practice was ad-hoc consultations at the discretion of the primary provider.

Post-Intervention Period: Artificial intelligence-driven clinical decision support

The technical architecture that enabled the real-time, NLP CDS tool incorporated industry-leading and emerging technological capabilities. The NLP CDS infrastructure exports the notes from the EHR, organizes them and feeds them into an NLP pipeline, inputed the processed text features into the opioid screener deep learning model, and delivered the resultant scores back to the bedside electronic health record as a best practice alert.

Other: Opioid Misuse Screening with an Addiction Consult Service

Interventions

Opioid Misuse Screening with an Addiction Consult Service OTHER

Opioid Misuse Screening with an addiction consult service for brief intervention/motivational interviewing (MI), medication assisted treatment (MAT), or referral to substance use treatment as an outpatient.

Post-Intervention Period: Artificial intelligence-driven clinical decision support

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults hospitalized at University of Wisconsin Hospital (UW Health)

You may qualify if:

• Adults hospitalized at University of Wisconsin Hospital (UW Health)

Sponsors & Collaborators

• University of Wisconsin, Madison: lead

Study Sites (1)

University of Wisconsin Hospital (UW Health)

Madison, Wisconsin, 53792, United States

Location

Related Publications (5)

• 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

• Sharma B, Dligach D, Swope K, Salisbury-Afshar E, Karnik NS, Joyce C, Afshar M. Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients. BMC Med Inform Decis Mak. 2020 Apr 29;20(1):79. doi: 10.1186/s12911-020-1099-y.

  PMID: 32349766 BACKGROUND

• Afshar M, Adelaine S, Resnik F, Mundt MP, Long J, Leaf M, Ampian T, Wills GJ, Schnapp B, Chao M, Brown R, Joyce C, Sharma B, Dligach D, Burnside ES, Mahoney J, Churpek MM, Patterson BW, Liao F. Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. JMIR Med Inform. 2023 Apr 20;11:e44977. doi: 10.2196/44977.

  PMID: 37079367 BACKGROUND

• Afshar M, Resnik F, Joyce C, Oguss M, Dligach D, Burnside ES, Sullivan AG, Churpek MM, Patterson BW, Salisbury-Afshar E, Liao FJ, Goswami C, Brown R, Mundt MP. Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults. Nat Med. 2025 Jun;31(6):1863-1872. doi: 10.1038/s41591-025-03603-z. Epub 2025 Apr 3.

  PMID: 40181180 RESULT

• Afshar M, Resnik F, Joyce C, Oguss M, Dligach D, Burnside E, Sullivan A, Churpek M, Patterson B, Salisbury-Afshar E, Liao F, Brown R, Mundt M. Outcomes and Cost-Effectiveness of an EHR-Embedded AI Screener for Identifying Hospitalized Adults at Risk for Opioid Use Disorder. Res Sq [Preprint]. 2024 Oct 14:rs.3.rs-5200964. doi: 10.21203/rs.3.rs-5200964/v1.

  PMID: 39483915 DERIVED

Related Links

• Github for SMART AI

MeSH Terms

Conditions

Opioid-Related Disorders

Condition Hierarchy (Ancestors)

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

Study Officials

• Majid Afshar

  University of Wisconsin, Madison

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 6, 2023
• First Posted: February 27, 2023
• Study Start: March 6, 2023
• Primary Completion: November 1, 2023
• Study Completion: December 31, 2023
• Last Updated: April 23, 2025

Record last verified: 2024-01

Locations

US (1)