Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.

Journal: BMC medical informatics and decision making
Published Date: Apr 14, 2015

Abstract

BACKGROUND: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial.

Authors

  • Yizhao Ni
    Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Jordan Wright
    Cancer and Blood Disease Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
  • John Perentesis
    Cancer and Blood Disease Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
  • Todd Lingren
    Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Louise Deleger
    Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.
  • Megan Kaiser
    Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.
  • Isaac Kohane
    Harvard Medical School, Boston Department of Neurology, Massachusetts General Hospital, Boston.
  • Imre Solti
    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.

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