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.
