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Data-driven Identification for Substance Misuse

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03833804
Recruitment Status : Not yet recruiting
First Posted : February 7, 2019
Last Update Posted : April 19, 2022
Sponsor:
Collaborator:
Rush University Medical Center
Information provided by (Responsible Party):
University of Wisconsin, Madison

Brief Summary:
The investigators propose to develop an open-source, publicly available machine learning model that health systems could download and apply to their electronic health record data marts to screen for substance misuse in their patients. The investigators hypothesize that the natural language processing algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for screening, brief intervention, and referral to treatment.

Condition or disease Intervention/treatment Phase
Substance Use Substance Abuse Substance-Related Disorders Other: Processing of clinical notes in the EHR data collected during routine care Not Applicable

Detailed Description:

In 2016, nearly 30% hospital discharges in the United States (US) had a major diagnostic category for a substance-use related condition. Substance misuse ranks second among principal diagnoses for unplanned 7-day hospital readmission rates. Despite the availability of Screening, Brief Intervention, and Referral to Treatment (SBIRT) interventions, substance misuse is not part of the admission routine and only a minority of patients are screened for substance misuse in the hospital setting. This is particularly problematic, since among hospitalized inpatients, the prevalence of substance misuse is estimated to be as high as 25%, greater than either the general population or outpatient setting. Practical screening methods tailored for the hospital setting are needed.

In the advent of Meaningful Use in the electronic health record (EHR), efficiency for alcohol detection may be improved by leveraging data collected during usual care. Documentation of substance use is common and occurs in over 96% of provider admission notes, but their free text format renders them difficult to mine and analyze. Natural Language Processing (NLP) and machine learning are subfields of artificial intelligence (AI) that provide a solution to analyze text data in the EHR to identify substance misuse. Modern NLP has fused with machine learning, another sub-field of artificial intelligence focused on learning from data. In particular, the most powerful NLP methods rely on supervised learning, a type of machine learning that takes advantage of current reference standards to make predictions about unseen cases

In the earlier version of an NLP and machine learning tool, the investigators successfully used data from clinical notes collected in the first 24 hours of hospital admission to reach a sensitivity and specificity above 70% for identifying alcohol misuse. With nearly 36 million hospital admissions in 2016, a substance misuse classifier has potential to impact millions.

In this study, the aim is to prospectively implement a substance misuse classifier to examine its effectiveness against current practice of all hospitalized adult patients at a tertiary health system. The health system has a mature screening system to examine substance misuse classifier performance against current practice of questionnaire screening.

The hypothesis is that the substance misuse classifier may provide a standardized, interoperable, and accurate approach to screen hospitalized patients. Successful implementation of the classifier in hospitalized patients is a step towards an automated and comprehensive universal screening system for substance misuse.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 34800 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Intervention Model Description: Quasi-experimental design as an interrupted time series
Masking: None (Open Label)
Masking Description: No masking as the manual screen is already part of usual care and the automated screen will become usual care in the post-period of the pre-post design.
Primary Purpose: Screening
Official Title: Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients
Estimated Study Start Date : August 1, 2023
Estimated Primary Completion Date : January 30, 2025
Estimated Study Completion Date : March 30, 2025

Arm Intervention/treatment
Experimental: NLP (natural language processing) pre-screen
Automated processing of clinical notes collected during routine care in first 24 hours of hospital admission to identify individuals at-risk for substance misuse to receive standard-of-care full screening and assessment, brief intervention, or referral to treatment (SBIRT) intervention.
Other: Processing of clinical notes in the EHR data collected during routine care
Clinical notes collected in the first day of hospital admission during usual care as input to natural language processing and machine learning algorithm.




Primary Outcome Measures :
  1. Proportion of patients that had a universal screen positive and received SBIRT (screening, brief intervention, or referral to treatment) [ Time Frame: 54 months ]
    The primary outcome is the proportion of patients who received SBIRT after a positive universal screen for being at risk for substance misuse. The design is an interrupted time-series prospective observational study.


Secondary Outcome Measures :
  1. All-cause re-hospitalizations following 6-months from the Index hospital encounter [ Time Frame: 12 months enrollment with 6 months follow-up for rehospitalization ]
    We will compare healthcare utilization outcomes in all patients between pre- and post-periods controlling for all patient demographic and clinical characteristics.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 89 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Ages 18 years old to 89 years old
  • Inpatient status during hospitalization
  • Length of stay greater than 24 hours

Exclusion Criteria:

  • Cannot participate in the usual care SBIRT intervention
  • Death or obtunded during first 24 hours of admission
  • Discharged against medical advice
  • Transferred from another acute care hospital
  • Transferred to another acute care hospital

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03833804


Contacts
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Contact: Majid Afshar, MD 3125459462 majid.afshar@wisc.edu

Sponsors and Collaborators
University of Wisconsin, Madison
Rush University Medical Center
Additional Information:
Publications of Results:
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Responsible Party: University of Wisconsin, Madison
ClinicalTrials.gov Identifier: NCT03833804    
Other Study ID Numbers: 211165
A534285 ( Other Identifier: UW Madison )
SMPH/MEDICINE ( Other Identifier: UW Madison )
First Posted: February 7, 2019    Key Record Dates
Last Update Posted: April 19, 2022
Last Verified: April 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: The patient data are protected health information and unavailable to public but the algorithm will be shared. The investigators will serialize our best models developed using either pickle (a Python native mechanism for object serialization) or joblib (https://pythonhosted.org/joblib/) and write software that will be capable of reloading them and making predictions. The software will be distributed via github.com or similar web-based software hosting service.
Supporting Materials: Analytic Code
Time Frame: 12 months after completion of study and available for at least five years on github.com
URL: http://github.com

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University of Wisconsin, Madison:
natural language processing
machine learning
artificial intelligence
clinical decision support
unhealthy alcohol use
opioid use disorder
illicit drug use
Additional relevant MeSH terms:
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Substance-Related Disorders
Chemically-Induced Disorders
Mental Disorders