# Automating eligibility assessment and enrollment for sugammadex administration within an integrated perioperative workflow

## TL;DR

This study shows that an automated system can help identify eligible patients for clinical trials in surgical settings, reducing the need for manual screening.

## Contribution

The paper introduces a scalable, EHR-integrated system for automated clinical trial enrollment in high-volume surgical workflows.

## Key findings

- The system identified 10,592 eligible patients and achieved 51.2% provider adherence to prompts.
- BPA allocation accuracy was 69.7% in the post-anesthesia care unit and 59.4% including ICU transfers.
- Workflow factors, not patient characteristics, were stronger predictors of provider adherence.

## Abstract

Traditional clinical trial enrollment relies on manual screening and coordinator-led recruitment, creating scalability barriers in high-volume perioperative environments. This study evaluated whether a fully automated, electronic health record (EHR)-integrated clinical decision support (CDS) system could identify eligible patients and engage clinicians in real time without manual screening or dedicated research staff.

In this prospective implementation study, predefined respiratory-risk criteria were computed within the UCLA Perioperative Data Warehouse and transmitted to the EHR via Healthcare Level Seven interfaces. Patients meeting inclusion criteria automatically triggered Best Practice Advisories (BPAs) recommending an intervention. Outcomes included system accuracy in eligibility identification, provider adherence to BPA recommendations, and technical performance metrics.

The automated system processed 10 592 eligible patients and achieved 51.2% provider adherence (5424 patients) to CDS prompts without coordinator involvement. BPA allocation accuracy was 69.7% among patients recovering in the post-anesthesia care unit and 59.4% when including unanticipated ICU transfers. Adherence varied significantly by care team composition, with full teams (attending + CRNA + resident) achieving 57.4% adherence compared with 42.2% for solo attendings. Workflow factors were stronger predictors of adherence than patient clinical characteristics, indicating minimal selection bias.

Fully automated, EHR-integrated CDS can enable large-scale, workflow-embedded enrollment into implementation-focused studies. While not a substitute for research designs requiring consent or randomization, this framework demonstrates a scalable approach for automated prescreening and CDS-driven prompting that reduces reliance on coordinator-dependent processes and supports real-world implementation science.