title: Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.
abstract: OBJECTIVE
  (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials.
  
  
  METHODS
  We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial-patient matches and a reference standard of historical trial-patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed.
  
  
  RESULTS
  Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial-patient matches.
  
  
  CONCLUSIONS
  By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Yizhao
authors.initials: Y
authors.lastname: Ni
authors.affiliation: Division of Pediatric Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Kennebeck
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA Division of Pediatric Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Judith W
authors.initials: JW
authors.lastname: Dexheimer
authors.affiliation: Division of Pediatric Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Constance M
authors.initials: CM
authors.lastname: McAneney
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Huaxiu
authors.initials: H
authors.lastname: Tang
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Haijun
authors.initials: H
authors.lastname: Zhai
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA James M Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
mesh_terms: D001185:Artificial Intelligence; D002986:Clinical Trials as Topic; D017598:Efficiency, Organizational; D004608:Eligibility Determination; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D018579:Patient Selection
keywords: Automated Clinical Trial Eligibility Screening; Information Extraction; Machine Learning; Natural Language Processing