Improving Eligibility Prescreening for Alzheimer's Disease and Related Dementias Clinical Trials with Natural Language Processing

日益流行的阿尔茨海默病和相关痴呆（ADRD）提出了重大的财政

The increasing prevalence of Alzheimer’s disease and related dementias (ADRD) presents major financial and care delivery challenges to the United States (US) healthcare system. There are currently an estimated 5.8 million Americans age 65 and older living with ADRD, with a projected increase to 13.8 million by 2050. Deaths associated with ADRD increased by 146.2% from 2000 to 2018, making it the sixth-leading cause of death in the US and the only disease in the top 10 causes of death that cannot be prevented, cured, or even slowed. Further complicating treatment advances is that on average, ADRD disease-modifying treatment development requires 13 years, with a failure rate of new therapies of more than 99%. A major bottleneck which contributes to this high failure rate is eligibility prescreening, which involves costly, time-consuming, and inefficient manual review of complex clinical data sources by clinical research staff. Natural language processing (NLP), an informatics approach used to extract relevant data from a variety of structured and unstructured data types, may improve eligibility prescreening for ADRD clinical trials. NLP has been used to identify potentially eligible patients in other disease-specific clinical trials that resulted to prompting research teams when appropriate research is available for specific patients, yet this has not been utilized in ADRD clinical trials. The proposed study will evaluate the clinical research staff’s technology adoption of an NLP-driven eligibility prescreening tool for ADRD clinical trials. Criteria2Query (C2Q), a novel open source NLP-driven eligibility prescreening tool, was developed to translate free-text eligibility criteria to standards-based cohort definition queries. In the proposed mixed-methods study, clinical research staff will participate in usability testing, and accuracy and efficiency evaluation of ADRD clinical trial eligibility prescreening using C2Q for patients seen in an Aging and Dementia clinical practice. Guided by the adapted Fit between Individuals, Task, and Technology Framework, the specific aims are to: (1) examine the usability of an NLP-driven tool for ADRD clinical trial eligibility prescreening, and (2) assess the efficiency and accuracy of eligibility prescreening done by clinical research staff using an NLP-driven tool for ADRD clinical trials. The proposed aims are consistent with the priorities of the Agency for Healthcare Research and Quality (AHRQ) in supporting research to increase accessibility and affordability of health care by examining innovative market approaches to care delivery. Findings from the proposed study have the potential to produce key findings for successful ADRD clinical research recruitment to accelerate knowledge discovery and development of a disease-modifying treatment for ADRD. Lastly, R36 dissertation award will provide a valuable opportunity for a pre-doctoral student to build a strong foundation for her long-term goal of becoming an independent nurse scientist with expertise in leveraging informatics in clinical research for the aging population.