title: Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.
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.
  
  
  METHODS
  We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated.
  
  
  RESULTS
  Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%).
  
  
  CONCLUSIONS
  By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.
authors.affiliation: Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA. Yizhao.Ni@cchmc.org.
authors.forename: Yizhao
authors.initials: Y
authors.lastname: Ni
authors.affiliation: Cancer and Blood Disease Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
authors.forename: Jordan
authors.initials: J
authors.lastname: Wright
authors.affiliation: Cancer and Blood Disease Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
authors.forename: John
authors.initials: J
authors.lastname: Perentesis
authors.affiliation: Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.affiliation: Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.
authors.forename: Louise
authors.initials: L
authors.lastname: Deleger
authors.affiliation: Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.
authors.forename: Megan
authors.initials: M
authors.lastname: Kaiser
authors.affiliation: Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
authors.forename: Isaac
authors.initials: I
authors.lastname: Kohane
authors.affiliation: Cincinnati Children's Hospital Medical Center, Department of Biomedical Informatics, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, USA.|James M Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
mesh_terms: D002648:Child; D002986:Clinical Trials as Topic / Q000379:methods*; D004608:Eligibility Determination / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D009369:Neoplasms / Q000628:therapy*; D018579:Patient Selection