Evidence map›Paper›PMID 41332823›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults.

Juan C Rojas, Cara Joyce, Talar W Markossian, Vaishvik Chaudhari, Mia R McClintic, Fatima Castro, A J Fairgrieve, Dmitriy Dligach, Madeline K Oguss, Matthew M Churpek and 2 more

Registry-linked trialAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03833804 (Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03833804 nacompletednot on this map

Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients

TypeinterventionalSponsorUniversity of Wisconsin, MadisonRan2022 to 2024Enrolled64,996ConditionsSubstance Use, Substance Abuse, Substance-Related DisordersArmsProcessing of clinical notes in the EHR data collected during routine care
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Juan C RojasDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Rush University, Chicago, IL, United States.ORCID 0000-0002-8561-4575
Cara JoyceDepartment of Medicine, Loyola University Chicago, Chicago, IL, United States.ORCID 0000-0003-0468-8271
Talar W MarkossianDepartment of Public Health Sciences, Loyola University Chicago, Maywood, IL, United States.ORCID 0000-0002-8147-7083
Vaishvik ChaudhariDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Rush University, Chicago, IL, United States.ORCID 0009-0006-7507-6634
Mia R McClinticDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Rush University, Chicago, IL, United States.ORCID 0009-0005-3502-7435
Fatima CastroDepartment of Psychiatry and Behavioral Sciences, Rush University, Chicago, IL, United States.
A J FairgrieveDepartment of Psychiatry and Behavioral Sciences, Rush University, Chicago, IL, United States.
Dmitriy DligachDepartment of Computer Science, Loyola University Chicago, Chicago, IL, United States.ORCID 0000-0002-2585-2707
Madeline K OgussDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-4983-8109
Matthew M ChurpekDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-4030-5250
Jenna NikolaidesDepartment of Psychiatry and Behavioral Sciences, Rush University, Chicago, IL, United States.ORCID 0000-0002-5271-9813
Majid AfsharDivision of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-6368-4652

Funding

Data Driven Strategies for Substance Misuse Identification in Hospitalized PatientsR01DA051464 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI Majid Afshar · 2020 to 2026
$4.5M
NIDA NIH HHS R01 DA051464
6 · The paper itself

Abstract

Importance: Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)-assisted screening during clinical implementation is needed to determine clinical and economic performance. Objective: To assess whether an AI-based screening program with the Substance Misuse Algorithm for Referral to Treatment Using Artificial Intelligence (SMART-AI) maintained delivery of addiction-related services compared with manual screening and to evaluate readmissions and costs. Design Setting and Participants: Prospective, quasi-experimental pre-post study at a large academic medical center in Chicago, Illinois between 2022 and 2025. The pre-implementation period (manual screening) included 31,432 hospitalizations, and the post-implementation period with AI augmentation (SMART-AI) included 33,564. Interventions/Exposures: During the post-implementation period, SMART-AI screened clinical documentation within 24 hours of admission to identify patients at-risk for a substance use disorder and notified the Substance Use Intervention Team. In the pre-implementation period, screening relied on manual processes, with nurses and social workers screening with standardized questionnaires. Main Outcomes and Measures: The primary outcome was receipt of ≥1 addiction-related service (initiation or adjustment of medication for alcohol or opioid use disorder; brief intervention/motivational interviewing; naloxone dispensing; or a completed addiction medicine consultation). The prespecified noninferiority margin was -0.5 percentage points (1-sided α = 0.025). Secondary outcomes included 6-month readmission, discharge against medical advice, and program costs. Results: Addiction-related services were received in 1,189 of 31,432 hospitalizations (3.8%) during manual screening and 1,144 of 33,564 (3.4%) during SMART-AI (difference, -0.4 percentage points; 95% CI, -0.7 to -0.1; P = 0.20). The lower limit of the confidence interval was below the noninferiority margin, so noninferiority was not achieved. Six-month readmissions across all hospitalizations occurred in 9,586 patients (30.5%) in the manual period and 10,244 patients (30.5%) in the SMART-AI period (P = 0.95), and discharge against medical advice did not differ (1.3% v. 1.1%). Among patients who received a SUIT intervention (n = 2,296), 6-month readmission occurred in 41.3% (485/1,175) during usual care versus 37.0% (415/1,121) during SMART-AI (odds ratio: 0.86, 95% CI: 0.73-1.03, p=0.10). Program costs over a 1-year period were $6,166.71 lower after SMART-AI automation. Conclusions and Relevance: AI-assisted screening did not meet the prespecified non-inferiority criterion for maintaining service delivery, but it was associated with maintaining secondary outcomes among patients screened for substance use disorder with lower program costs. Findings support the feasibility and potential value of automated screening at scale. Trial Registration: ClinicalTrials.gov Identifier: NCT03833804.

Identifiers

PMID41332823
PMCPMC12668069

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.