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.
Highlights
Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources
The workload was reduced by more than 90% to 4 trials per patient when the complete ES algorithm (DX/ICD-9 + NOTE) was leveraged
Performance analysis Our results show that a fully-automated ES algorithm, that relies on the narrative eligibility criteria of clinical trials and the information from patient Electronic Health Record (EHR), could achieve notable workload reduction in both trial-centered patient cohort identification (85%) and patient-centered trial recommendation compared with demographics-based screening (Table 1)
Summary
Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Several reports have described positive experiences leveraging electronic health record (EHR) information to facilitate trial recruitment, eligibility screening (ES) is still conducted manually in most cases [1,2,3]. The factor that most clinical practices are not staffed for manual patient screening is a challenge for clinical trial recruitment. For these reasons, automatically prescreening and identifying trial-patient matches, on the basis of EHR information, promises great benefits for translational research. Patient-centered trial recommendation is valuable, if the key barrier to physician participation is the time required for identifying appropriate trials for individual patients from a large pool of active trials [18]
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