Dataset Information

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

ABSTRACT: 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.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.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%).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.

SUBMITTER: Ni Y

PROVIDER: S-EPMC4407835 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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

Ni Yizhao Y Wright Jordan J Perentesis John J Lingren Todd T Deleger Louise L Kaiser Megan M Kohane Isaac I Solti Imre I

BMC medical informatics and decision making 20150414

<h4>Background</h4>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 identi ...[more]

PMID: 25881112

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Project description:ObjectiveEfficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening.MethodsWe studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1) structured features, 2) the counts of NLP concepts from notes, 3) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1) one or more RA ICD code and no ICD codes related to exclusion criteria (ScreenRAICD1 +EX ) and 2) two or more RA ICD codes (ScreenRAICD2 ). To test the portability, we trained the algorithm at one institution and tested it at the other.ResultsIn total, 3359 patients at Brigham and Women's Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, ScreenRAICD2 reduced patients for chart review by 2.7% to 11.3%; ScreenRAICD1+EX reduced patients for chart review by 63% to 65% but excluded 22% to 27% of eligible patients.ConclusionThe ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.

Project description:Background Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood.Objectives We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability.Methods The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem.Results The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category "Diagnosis and Study" contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria.Conclusion Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed.

Project description:BackgroundA pediatric cancer diagnosis and its treatment can have a detrimental effect on the mental health of children and their families. Screening to identify psychosocial risk in families has been recognized as a standard of care in pediatric oncology, but there has been limited clinical application of this standard thus far. A significant impediment to the implementation of psychosocial screening is the dearth of information on how to translate psychosocial screening to clinical practice, and specifically, how to follow-up from screening results. This manuscript aims to describe a protocol of a new intervention examining the feasibility and acceptability of mapping via a Psychosocial Navigator (PSN) psychosocial screening results to specific recommendations of resources for families based on measured risk for psychosocial distress and mental health symptoms.MethodsThe pilot randomized control trial (RCT) consists of dyads of youth (10-17 years) newly diagnosed with cancer and their primary caregiver. This RCT includes two arms (intervention and control group), with each group completing measurements near diagnosis and 1 year later. After the initial assessment, dyads in the intervention group receive monthly screening results and recommendations from the study PSN that are tailored to these results. The patient's primary healthcare team (nurse, social worker, oncologist) also receive the risk, distress, and mental health results as well as the recommendations from the PSN.DiscussionThis study addresses a significant barrier to the implementation of psychosocial screening in pediatric oncology: specifically, the limited knowledge of how to follow-up from screening results. Findings from this pilot will inform a future definitive RCT to test the effectiveness of the intervention on patient and family mental health outcomes. This project has implications for enhancing clinical care in pediatric oncology, as well as other pediatric populations.Strengths and limitations of this studyThis is the first study of screening and follow-up using a psychosocial navigator. This study involves both patient and caregiver report. The small sample size necessitates a future larger study to investigate the effects of intervention.Trial registrationNCT04132856 , Registered 10 October 2019-retrospectively registered.

Dataset Information

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

Publications

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

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