Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults

Abstract

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for patients at risk for an OUD to prevent complications is not standard practice in many hospitals, leading to missed opportunities for intervention. The adoption of electronic health records (EHRs) and advancements in artificial intelligence (AI) offer a scalable approach to systematically identify at-risk patients for evidence-based care. This pre-post quasi-experimental study evaluated whether an AI-driven OUD screener embedded in the EHR was non-inferior to usual care in identifying patients for addiction medicine consultations, aiming to provide a similarly effective but more scalable alternative to human-led ad hoc consultations. The AI screener used a convolutional neural network to analyze EHR notes in real time, identifying patients at risk and recommending consultations. The primary outcome was the proportion of patients who completed a consultation with an addiction medicine specialist, which included interventions such as outpatient treatment referral, management of complicated withdrawal, medication management for OUD and harm reduction services. The study period consisted of a 16-month pre-intervention phase followed by an 8-month post-intervention phase, during which the AI screener was implemented to support hospital providers in identifying patients for consultation. Consultations did not change between periods (1.35% versus 1.51%, P < 0.001 for non-inferiority). In secondary outcome analysis, the AI screener was associated with a reduction in 30-day readmissions (odds ratio: 0.53, 95% confidence interval: 0.30-0.91, P = 0.02) with an incremental cost of US$6,801 per readmission avoided, demonstrating its potential as a scalable, cost-effective solution for OUD care. ClinicalTrials.gov registration: NCT05745480 .

Fig. 1 ∣. Flow diagram of patient hospitalizations for pre-implementation and post-implementation periods of BPA in the EHR for screening unhealthy opioid use with associated interventions.

Post-implementation addiction medicine consults are shown regardless of the AI screener. Multiple hospitalizations per patient are possible. MOUD, medications for OUD. Each completed consultation could include: (1) opioid use assessment and brief behavioral intervention with motivational interviewing; (2) initiation, continuation or adjustment of MOUD; (3) harm reduction services including naloxone and fentanyl test strips; and/or (4) discharge planning with referral to community-based treatment.

Fig. 2 ∣. Image of BPA in the EHR for recommending addiction medicine consult order and Clinical Opiate Withdrawal Scale.

The finalized BPA includes recommendations for both an addiction medicine consultation and the use of the Clinical Opiate Withdrawal Scale (COWS). The versions presented here were deployed into production and fully embedded within the Epic EHR system. The alert is triggered only when the AI model’s screening threshold is met. This final version incorporates user feedback gathered during the initial PDSA cycles of the hybrid effectiveness–implementation phase, ensuring alignment with clinical workflow and provider needs.

Fig. 3 ∣. Single-note illustration of AI screener.

A visualization example demonstrates the individual-level clinical utility of the extracted medical concepts as concept unique identifiers (CUIs) mapped from the note to the Unified Medical Language System Metathesaurus at the National Library of Medicine. Integrated gradients assign an importance score to each input CUI by approximating the integral of the gradients of the AI model’s output to the inputs. This method compares the model’s prediction on the actual input with a baseline generic note with random CUIs, which typically represent the neutral state. The gradients are calculated at several points along the path from the baseline to the actual input. The integral of these gradients gives an attribution score for each CUI, indicating its contribution to the final prediction. Warmer background colors indicate an increased likelihood of unhealthy opioid use, while cooler colors reflect a decreased probability. The final predicted probability was 0.6753, which is above the threshold for a screen positive; whereas a baseline note with generic tokens for medical concepts had an expectedly lower probability of 0.0693. The process of approximating the integral introduces some approximation error, which depends on the number of steps used in the integration process. More steps typically reduce this error but increase computational complexity. In our case, the CUIs with higher attribution scores strongly influenced the predicted probability of unhealthy opioid use, highlighting their clinical relevance.