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

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AI-assisted screening for substance use disorder did not meet non-inferiority for service delivery but maintained outcomes and lowered costs. This study shows automated screening is feasible and valuable for large-scale implementation.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Substance Use Disorder Treatment

Background:

  • Manual inpatient screening for substance misuse is resource-intensive and inconsistently applied.
  • Evaluating artificial intelligence (AI)-assisted screening is crucial for assessing clinical and economic performance in real-world settings.

Purpose of the Study:

  • To assess if an AI-based screening program (SMART-AI) maintained addiction-related service delivery compared to manual screening.
  • To evaluate the impact of AI-assisted screening on patient readmissions and healthcare costs.

Main Methods:

  • A prospective, quasi-experimental pre-post study was conducted at a large academic medical center.
  • The study compared manual screening (N=31,432 hospitalizations) with AI-augmented screening using SMART-AI (N=33,564 hospitalizations).
  • Primary outcome was receipt of addiction-related services; secondary outcomes included 6-month readmissions and program costs.

Main Results:

  • Addiction-related services were received by 3.8% (manual) vs. 3.4% (SMART-AI), not meeting the non-inferiority margin (-0.4 pp; P=0.20).
  • Six-month readmissions (30.5%) and discharge against medical advice (1.3% vs. 1.1%) did not differ between groups.
  • Program costs were reduced by $6,166.71 annually with SMART-AI automation.

Conclusions:

  • AI-assisted screening did not meet non-inferiority for service delivery but maintained secondary outcomes.
  • The findings support the feasibility and potential value of automated substance use disorder screening at scale.
  • SMART-AI demonstrated potential for cost savings in healthcare settings.