Opioid Use Disorder Clinical Trial
Official title:
The Evaluation of a Real-time Natural Language Processing Decision Support Tool for Screening Opioid Misuse With Addiction Consult Intervention for Hospitalized Adults
Verified date | January 2024 |
Source | University of Wisconsin, Madison |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Completed |
Enrollment | 47502 |
Est. completion date | December 31, 2023 |
Est. primary completion date | November 1, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Adults hospitalized at University of Wisconsin Hospital (UW Health) Exclusion Criteria: - |
Country | Name | City | State |
---|---|---|---|
United States | University of Wisconsin Hospital (UW Health) | Madison | Wisconsin |
Lead Sponsor | Collaborator |
---|---|
University of Wisconsin, Madison |
United States,
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. — View Citation
Protocol for a Real-Time Electronic Health Record Implementation of a Natural Language Processing and Deep Learning Clinical Decision Support Tool: A Use-Case for an Opioid Misuse Screener in Hospitalized Adults Majid Afshar, Sabrina Adelaine, Felice Resnik, Marlon P. Mundt, John Long, Margaret Leaf, Theodore Ampian, Graham J Wills, Benjamin Schnapp, Michael Chao, Randy Brown, Cara Joyce, Brihat Sharma, Dmitriy Dligach, Elizabeth S. Burnside, Jane Mahoney, Matthew M Churpek, Brian W. Patterson, Frank Liao doi: https://doi.org/10.1101/2022.12.04.22282990
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | 30-day unplanned hospital readmission rate | Criteria for unplanned hospital readmissions were adopted from the Centers for Medicare & Medicaid Services. | Up to 6 months |
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