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Cancer Inform. 2014 Dec 4;13:157-66. doi: 10.4137/CIN.S19454. eCollection 2014.

Trial prospector: matching patients with cancer research studies using an automated and scalable approach.

Author information

1
Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
2
Division of Hematology and Oncology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. ; University Hospitals Case Medical Center, Seidman Cancer Center, Cleveland, OH, USA.
3
University Hospitals Case Medical Center, Seidman Cancer Center, Cleveland, OH, USA. ; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
4
Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
5
Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA. ; Institute for Computational Biology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
6
Division of Hematology and Oncology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. ; University Hospitals Case Medical Center, Seidman Cancer Center, Cleveland, OH, USA. ; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
7
Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. ; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.

Abstract

Cancer is responsible for approximately 7.6 million deaths per year worldwide. A 2012 survey in the United Kingdom found dramatic improvement in survival rates for childhood cancer because of increased participation in clinical trials. Unfortunately, overall patient participation in cancer clinical studies is low. A key logistical barrier to patient and physician participation is the time required for identification of appropriate clinical trials for individual patients. We introduce the Trial Prospector tool that supports end-to-end management of cancer clinical trial recruitment workflow with (a) structured entry of trial eligibility criteria, (b) automated extraction of patient data from multiple sources, (c) a scalable matching algorithm, and (d) interactive user interface (UI) for physicians with both matching results and a detailed explanation of causes for ineligibility of available trials. We report the results from deployment of Trial Prospector at the National Cancer Institute (NCI)-designated Case Comprehensive Cancer Center (Case CCC) with 1,367 clinical trial eligibility evaluations performed with 100% accuracy.

KEYWORDS:

clinical decision support system; clinical oncology; clinical trial; gastrointestinal cancer; patient recruitment

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