AI for Alcohol Misuse in the EHR
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|ClinicalTrials.gov Identifier: NCT03833804|
Recruitment Status : Not yet recruiting
First Posted : February 7, 2019
Last Update Posted : February 7, 2019
|Condition or disease||Intervention/treatment|
|Alcohol Drinking Alcohol; Harmful Use Alcohol Abuse Trauma||Other: Processing of clinical notes in the EHR data collected during routine care|
Alcohol misuse accounts for 1 in 10 deaths in the United States and rates of misuse have risen 9% between 2002 and 2012 with 18% of United States population reporting a pattern of regular binges. As many as 33% of patients with trauma encounters have alcohol misuse.3 This burden can be reduced, however - screening, brief intervention, and referral to treatment (SBIRT) programs reduce trauma recidivism by nearly 50%.
The National Academy of Sciences has recommended the Alcohol Use Disorders Identification Test (AUDIT) for screening in the electronic health record (EHR). However, the investigators performed a survey across multiple health systems in Chicago and found few are using the AUDIT and substantial heterogeneity exists in the format of data capture for alcohol. At Loyola University's Trauma Center which uses the AUDIT in its SBIRT program, full-time dedicated screeners completed the screen in 56% of patients arriving in the emergency department. The most common reason for missing patients was inability to screen during off-duty hours.
The clinical narrative in the EHR captured during routine care is a rich source of information that is not currently used in a formal manner to screen for alcohol misuse. For example, the social history in the providers' notes typically incorporate patients' answers to questions about alcohol use, but there are no automated methods currently available to make use of these data. Natural Language Processing (NLP) and machine learning are subfields of artificial intelligence (AI) that provide powerful tools to analyze unstructured data, such as free text, in the EHR. NLP is a set of computational methods for deriving meaning from human-generated texts that feeds into machine learning algorithms to learn and predict. The role of NLP for case identification in alcohol misuse is in its infancy; no peer-reviewed publications have yet examined NLP for this purpose.
The investigators hypothesize that the NLP algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for SBIRT.
The aim is to prospectively test the effectiveness of the NLP algorithm as a pre-screen tool for alcohol misuse in a trauma center. The investigators will use a pre-post comparison design at a Level I Trauma Center. After 12 months of using the NLP tool for daily automated pre-screens, the investigators will assess implementation fidelity of alcohol screening at the index hospital and examine health outcomes
|Study Type :||Observational|
|Estimated Enrollment :||1504 participants|
|Official Title:||Artificial Intelligence for the Identification of Alcohol Misuse From the Electronic Health Record|
|Estimated Study Start Date :||January 2021|
|Estimated Primary Completion Date :||March 2023|
|Estimated Study Completion Date :||April 2023|
Two nurses using routinely collected blood alcohol laboratory data on all trauma encounters to a Level I trauma center to identify individuals at-risk for alcohol misuse to receive standard-of-care screening, brief intervention, or referral to treatment (SBIRT) intervention.
NLP (natural language processing) pre-screen
Automated processing of clinical notes collected during routine care in first 24 hours of trauma admission to identify individuals at-risk for alcohol misuse to receive standard-of-care screening, 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 trauma admission during usual care as input to natural language processing and machine learning algorithm.
- Proportion of patients that received SBIRT (screening, brief intervention, or referral to treatment) [ Time Frame: 12 months ]The primary outcome is the proportion of patients who received SBIRT after pre-screening positive for being at-risk for alcohol misuse.
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
|Contact: Majid Afshar, MDfirstname.lastname@example.org|