ArticleJAMIA open2026
Automating eligibility assessment and enrollment for sugammadex administration within an integrated perioperative workflow.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Indexed as
Identifiers
What OpenQuestion holds
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.