Increasing Clinical Trial Enrollment: A Semi-Automated Patient Centered Approach

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DESCRIPTION (provided by applicant): The long-term objective of this research is to increase the clinical trial enrollment of US patients via a semi- automated, Natural Language Processing (NLP) based, interactive and patient-centered informatics application. The study design is prospective observational study. Scope is limited to cancer patients. There are three specific aims for this project. The first aim is to identify concepts that overlap between the electronic medical record's (EMR) clinical notes and the free text of clinical trial announcements. The PI will use the concepts to develop mapping frames that connect concepts in the text of trial announcements to those found in clinical notes in the medical record. When he has the mapping frames he will build the NLP module for the application. In the software development work he will utilize as many publicly available software components as possible. He will experiment with UIMA, GATE, MetaMap, Stanford Parser, NegEx algorithm and others. The PI will develop the tool around the National Library of Medicine's Unified Medical Language System knowledgebase. He will use Java for programming. The second aim is to create an algorithm that automatically generates questions to request information directly from the patient if the information is not available or accessible in the records. The third aim is to evaluate the in-vitro, laboratory performance of the application. For performance evaluation purposes the PI will recruit cancer care specialists to generate the gold standard lists of eligible clinical trials for study patients. He will publicly release the developed code at the end of the grant period. This K99/R00 project will serve the foundation for future R01 grant applications. The PI is fully committed to become faculty in the Clinical Research Informatics domain with a specialization in biomedical NLP. The support of the K99/R00 grant will enable him to acquire substantial formal training in Computational Linguistics while contributing to the body of knowledge of the Clinical Research Informatics field. The five-year grant support will ensure success in his endeavor. The proposed work is highly significant because the dismal clinical trial accrual rates (2-4 % nationally) hampers timely development of new drugs. In addition, studies show that physicians have statistically significant bias against elderly and minority patients to invite participation in clinical trials. The proposed project is synergistic with physician-centered efforts but the goal is to provide individualized, EMR based clinical trial recommendations directly to the patients. The results of this research will empower the patients and elevate their role in the decision making process.