Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center

2020 ◽  
pp. 50-59 ◽  
Author(s):  
J. Thaddeus Beck ◽  
Melissa Rammage ◽  
Gretchen P. Jackson ◽  
Anita M. Preininger ◽  
Irene Dankwa-Mullan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Language Processing ◽  
Medical Records ◽  
Cancer Center ◽  
Patients With Cancer ◽  
Attribute Extraction ◽  
Time Required ◽  
Manual Review

PURPOSE Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

scholarly journals Trial Prospector: Matching Patients with Cancer Research Studies Using an Automated and Scalable Approach

2014 ◽  
Vol 13 ◽  
pp. CIN.S19454 ◽  
Author(s):  
Satya S. Sahoo ◽  
Shiqiang Tao ◽  
Andrew Parchman ◽  
Zhihui Luo ◽  
Licong Cui ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Survival Rates ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Dramatic Improvement ◽  
Multiple Sources ◽  
Eligibility Criteria ◽  
Physician Participation ◽  
Time Required

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.

Evaluation of barriers to clinical trial enrollment through a novel pharmacy quality assurance tool

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 17026-17026
Author(s):  
D. C. Vamos ◽  
M. P. Kane ◽  
J. Nishioka ◽  
S. Lisi ◽  
J. R. Neceskas ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Performance Status ◽  
Pharmacy Service ◽  
Cancer Center ◽  
Limiting Factor ◽  
Available N ◽  
Patients With Cancer ◽  
Barriers To Enrollment ◽  
Pharmacy Quality

17026 Background: Clinical trials offer the best treatment for patients with cancer, yet less than 5 percent of adults and less than 60 percent of children are enrolled on clinical trials. To determine reasons for lack of enrollment on clinical trials and to assess areas for potential trial development, we designed a ‘non-protocol’ form for use at our center. Our goal was to assess deficiencies in our menu of trials, identify other barriers to enrollment, and to indirectly increase awareness of trials. Methods: Completion of a ‘non-protocol’ form was required by the pharmacy with the first set of new chemotherapy orders for all Cancer Institute of New Jersey ambulatory patients who were not enrolled on a clinical trial. The form required completion of one of three areas for lack of enrollment: trial availability, reason for ineligibility, or other reason for not enrolling the patient. Results: From June 2003 through December 2005, 474 forms were collected for patients not enrolled on a clinical trial. The median age of patients not enrolled on trial was 56 years (range 1 to 88 years) and females outnumbered males (69% vs 31%). Lack of trial availability limited enrollment for 51% of patients (n=241) while administration of standard therapy was listed for 10 patients. Of those patients where a trial was available (n=223), 65% (n=145) of patients were not eligible, most commonly due to performance status (n=55). The remaining 78 patients refused participation. To determine if implementation of this pharmacy service changed the reasons for lack of enrollment, the data was evaluated by year: Conclusion: Lack of trial availability has been a rate-limiting factor in enrollment on clinical trials at our center. The data generated from the implementation of this novel pharmacy service is of strategic importance to the center. It is reviewed with the tumor-focused groups of the cancer center to identify areas for developing clinical trials. [Table: see text] No significant financial relationships to disclose.

scholarly journals Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822198939
Author(s):  
Euisung Jung ◽  
Hemant Jain ◽  
Atish P Sinha ◽  
Carmelo Gaudioso
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Text Mining ◽  
Language Processing ◽  
Snomed Ct ◽  
Lexical Resources ◽  
Named Entities ◽  
Domain Experts ◽  
Trial Subject

A natural language processing (NLP) application requires sophisticated lexical resources to support its processing goals. Different solutions, such as dictionary lookup and MetaMap, have been proposed in the healthcare informatics literature to identify disease terms with more than one word (multi-gram disease named entities). Although a lot of work has been done in the identification of protein- and gene-named entities in the biomedical field, not much research has been done on the recognition and resolution of terminologies in the clinical trial subject eligibility analysis. In this study, we develop a specialized lexicon for improving NLP and text mining analysis in the breast cancer domain, and evaluate it by comparing it with the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). We use a hybrid methodology, which combines the knowledge of domain experts, terms from multiple online dictionaries, and the mining of text from sample clinical trials. Use of our methodology introduces 4243 unique lexicon items, which increase bigram entity match by 38.6% and trigram entity match by 41%. Our lexicon, which adds a significant number of new terms, is very useful for matching patients to clinical trials automatically based on eligibility matching. Beyond clinical trial matching, the specialized lexicon developed in this study could serve as a foundation for future healthcare text mining applications.

scholarly journals Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study (Preprint)

2020 ◽  
Author(s):  
Carlos R Oliveira ◽  
Patrick Niccolai ◽  
Anette Michelle Ortiz ◽  
Sangini S Sheth ◽  
Eugene D Shapiro ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Human Papillomavirus ◽  
Natural Language ◽  
Language Processing ◽  
Medical Records ◽  
Processing Algorithm ◽  
Accurate Identification ◽  
Pathology Reports ◽  
Manual Review ◽  
Natural Language Processing Algorithm

BACKGROUND Accurate identification of new diagnoses of human papillomavirus–associated cancers and precancers is an important step toward the development of strategies that optimize the use of human papillomavirus vaccines. The diagnosis of human papillomavirus cancers hinges on a histopathologic report, which is typically stored in electronic medical records as free-form, or unstructured, narrative text. Previous efforts to perform surveillance for human papillomavirus cancers have relied on the manual review of pathology reports to extract diagnostic information, a process that is both labor- and resource-intensive. Natural language processing can be used to automate the structuring and extraction of clinical data from unstructured narrative text in medical records and may provide a practical and effective method for identifying patients with vaccine-preventable human papillomavirus disease for surveillance and research. OBJECTIVE This study's objective was to develop and assess the accuracy of a natural language processing algorithm for the identification of individuals with cancer or precancer of the cervix and anus. METHODS A pipeline-based natural language processing algorithm was developed, which incorporated machine learning and rule-based methods to extract diagnostic elements from the narrative pathology reports. To test the algorithm’s classification accuracy, we used a split-validation study design. Full-length cervical and anal pathology reports were randomly selected from 4 clinical pathology laboratories. Two study team members, blinded to the classifications produced by the natural language processing algorithm, manually and independently reviewed all reports and classified them at the document level according to 2 domains (diagnosis and human papillomavirus testing results). Using the manual review as the gold standard, the algorithm’s performance was evaluated using standard measurements of accuracy, recall, precision, and F-measure. RESULTS The natural language processing algorithm’s performance was validated on 949 pathology reports. The algorithm demonstrated accurate identification of abnormal cytology, histology, and positive human papillomavirus tests with accuracies greater than 0.91. Precision was lowest for anal histology reports (0.87, 95% CI 0.59-0.98) and highest for cervical cytology (0.98, 95% CI 0.95-0.99). The natural language processing algorithm missed 2 out of the 15 abnormal anal histology reports, which led to a relatively low recall (0.68, 95% CI 0.43-0.87). CONCLUSIONS This study outlines the development and validation of a freely available and easily implementable natural language processing algorithm that can automate the extraction and classification of clinical data from cervical and anal cytology and histology.

scholarly journals Key Barriers to Clinical Trial Enrolment: A Survey of Clinical Trial Staff at an NCI Designated Cancer Center

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5864-5864
Author(s):  
Amany R. Keruakous ◽  
Adam S. Asch
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Cultural Bias ◽  
Cancer Center ◽  
Exclusion Criteria ◽  
Strict Inclusion ◽  
Trial Protocols ◽  
Trial Staff ◽  
Disease Site ◽  
Patient Enrolment

Background: Clinical trials, key elements of the processes that account for many of the recent advances in cancer care, are becoming more complex and challenging to conduct. The Stephenson Cancer Center (SCC) has been the lead accruer to NCI-LAP trials over the past three years, and in addition, fields investigator initiated and industry sponsored trials. To identify opportunities for continued improvement in clinical trial enrolment, we sought to identify the obstacles encountered by our clinical trial staff in these activities. Method: We conducted a survey of our research staff including all research nurses and disease site coordinators who participate in recruitment, screening, consenting, data collection and compliance. The survey, sent by email to the clinical trial list-serve at SCC (90 staff member), invited respondents to enumerate obstacles to patient participation in clinical trials. We then performed a follow up meeting with our research coordinators to clarify responses. A total of 26 responses from 90 respondents were received and tabulated by disease site. Results: The most commonly reported obstacles to enrolment were, in descending order: communication/language barriers, cultural bias, time/procedure commitment, and complexity of the trial protocol, financial logistics, comorbidities, and stringent trial criteria. Respondents identified 83 obstacles as frequently encountered obstacles to enrolment. The 83 reported obstacles were classified into 9 categories and organized by disease site as presented in tabular format (below). The most commonly identified obstacles to patient enrolment were communication and language barriers. In patients for whom Spanish is the primary language this was a universal obstacle, as there is a lack of consistent Spanish consents across the clinical trial portfolio. Cultural bias, as an obstacle was manifested as a general mistrust by prospective trial participants of experimental therapies and clinical trials. After communication and cultural bias as barriers, travel requirements and the associated expenses playing a role in patients from rural areas were identified as the most commonly encountered barrier. The complexity of trial protocols and the associated large number of clinic visits, frequent laboratory and imaging tests were also identified as common obstacles. Clinical trial complexity with strict inclusion and exclusion criteria and trial-specified biopsies were frequently cited. Implications: In this descriptive study, common barriers to patient enrolment in clinical trials were identified by clinical trial staff. Assessing barriers encountered by clinical trial staff is infrequently used as a metric for improving clinical trial enrolment, but provides important perspective. In our study, some obstacles are inherent in our patient populations, others appear to be actionable. Development of Spanish language consents and specific programs to overcome negative bias regarding clinical trials are potential areas for improvement. The complexity of clinical trial protocols and the increasingly strict inclusion/exclusion criteria, are issues that will require consideration and action at the level of the cooperative groups and industry. Disclosures No relevant conflicts of interest to declare.

Evaluating the Financial Impact of Clinical Trials in Oncology: Results From a Pilot Study From the Association of American Cancer Institutes/Northwestern University Clinical Trials Costs and Charges Project

2000 ◽  
Vol 18 (15) ◽  
pp. 2805-2810 ◽  
Author(s):  
Charles L. Bennett ◽  
Tammy J. Stinson ◽  
Victor Vogel ◽  
Lyn Robertson ◽  
Donald Leedy ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Pilot Study ◽  
Medical Care ◽  
Phase Ii ◽  
Third Party ◽  
Disease Stage ◽  
Cancer Center ◽  
Policy Debate ◽  
Economic Analyses

PURPOSE: Medical care for clinical trials is often not reimbursed by insurers, primarily because of concern that medical care as part of clinical trials is expensive and not part of standard medical practice. In June 2000, President Clinton ordered Medicare to reimburse for medical care expenses incurred as part of cancer clinical trials, although many private insurers are concerned about the expense of this effort. To inform this policy debate, the costs and charges of care for patients on clinical trials are being evaluated. In this Association of American Cancer Institutes (AACI) Clinical Trials Costs and Charges pilot study, we describe the results and operational considerations of one of the first completed multisite economic analyses of clinical trials. METHODS: Our pilot effort included assessment of total direct medical charges for 6 months of care for 35 case patients who received care on phase II clinical trials and for 35 matched controls (based on age, sex, disease, stage, and treatment period) at five AACI member cancer centers. Charge data were obtained for hospital and ancillary services from automated claims files at individual study institutions. The analyses were based on the perspective of a third-party payer. RESULTS: The mean age of the phase II clinical trial patients was 58.3 years versus 57.3 years for control patients. The study population included persons with cancer of the breast (n = 24), lung (n = 18), colon (n = 16), prostate (n = 4), and lymphoma (n = 8). The ratio of male-to-female patients was 3:4, with greater than 75% of patients having stage III to IV disease. Total mean charges for treatment from the time of study enrollment through 6 months were similar: $57,542 for clinical trial patients and $63,721 for control patients (1998 US$; P = .4) CONCLUSION: Multisite economic analyses of oncology clinical trials are in progress. Strategies that are not likely to overburden data managers and clinicians are possible to devise. However, these studies require careful planning and coordination among cancer center directors, finance department personnel, economists, and health services researchers.

scholarly journals Patients with cancer in the COVID-19 era: the clinical trial issue

2020 ◽  
Vol 106 (4) ◽  
pp. 271-272 ◽  
Author(s):  
Marcello Scarcia ◽  
Giuseppe Mario Ludovico ◽  
Angela Fortunato ◽  
Alba Fiorentino
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Physical Examination ◽  
Regulatory Agencies ◽  
Study Medication ◽  
Therapeutic Modalities ◽  
Patients With Cancer ◽  
Safety Guidelines ◽  
Trial Discontinuation

Coronavirus disease 2019 (COVID-19) hospital reorganization may result in reduced ability for the hospital to fully use its armamentarium for battling cancer. Thus different therapeutic modalities have been recommended. During the pandemic, despite regulatory agencies’ recommendations, several considerations and doubts remain for oncologic clinical trials. Considering patients who had been enrolled before the pandemic, and who plan to take the study medication, the situation becomes complicated. These patients should undergo monitoring visits, blood sampling, questionnaire, physical examination, and drug and radiation administration. To avoid deviations from the protocol and trial discontinuation, follow-up should be performed regularly, in concordance with safety guidelines. Here we report several considerations.

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