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

Abstract

BackgroundManual 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.MethodsWe 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.ResultsWithout 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%).ConclusionBy 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0149-3) contains supplementary material, which is available to authorized users.

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“…The key goals of this process are to (1) identify those populations who meet the inclusion criteria for a study, and (2) facilitate the most efficient workflow for enrollment of those patients into the correct trial [82]. Ni et al showed that a text-mining-based screening system could accomplish both goals [83]. They identified patient phenotypes using text mining of notes and billing code data to enable automated patient screening.…”

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“…Pakhomov and colleagues used it to identify patients suffering from angina pectoris [131] or heart failure [132]. Ni and colleagues used it to improve oncology trial eligibility screening [130], and Weng and Boland to represent and extract trial eligibility criteria [133,134]. Extracting information to improve treatment and follow-up of patients has been applied to pancreatic [135] and colon neoplasms detection [136], thromboembolism and incidental findings [137], adverse events and errors detection [137], and patients acuity prediction [138].…”

“…Various data and attribute values were extracted to support peripheral artery disease and heart failure research in the eMERGE network [128], and to support obesity research [129]. Study subjects recruitment is a constant struggle, and adding more detailed information extracted from unstructured data to existing diagnostic codes significantly improves it [130]. Pakhomov and colleagues used it to identify patients suffering from angina pectoris [131] or heart failure [132].…”

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“…Papers reviewed included the use of natural language processing on clinical notes, case-based reasoning, and automated analysis of audiograms [42][43][44][45]. The Internet is having an increased impact on patient recruitment.…”