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

阿尔茨海默氏病和相关痴呆症（ADRD）的发病率不断增加， 以及对美国（US）医疗保健系统的护理提供挑战。目前估计有 5.8 65岁及以上的美国人患有ADRD，预计到2050年将增加到1380万。 从2000年到2018年，与ADRD相关的死亡增加了146.2%，使其成为第六大疾病原因。 这是美国十大死亡原因中唯一一种无法预防、治愈甚至 慢下来。进一步复杂化的治疗进展是，平均而言，ADRD疾病改善治疗 开发需要13年时间，新疗法的失败率超过99%。的一大瓶颈 导致这种高失败率的原因是资格预审，这涉及昂贵，耗时， 临床研究人员对复杂临床数据源进行低效的人工审查。自然语言 自然语言处理（NLP）是一种信息学方法，用于从各种结构化和 非结构化数据类型，可以提高ADRD临床试验的资格预筛选。NLP已被用于 在其他疾病特异性临床试验中识别潜在合格患者，从而促进研究 当特定患者有适当的研究时，但尚未在ADRD中使用 临床试验 拟议的研究将评估临床研究人员采用NLP驱动的技术 ADRD临床试验的资格预筛选工具。Criteria2Query（C2Q），一个新的开源NLP驱动的 资格预筛选工具，用于将自由文本资格标准转换为基于标准的队列 定义查询。在拟定的混合方法研究中，临床研究人员将参与可用性研究 使用C2Q进行ADRD临床试验合格性预筛选的准确性和有效性评价， 在老龄化和痴呆症临床实践中观察到的患者。在个体之间的适应性匹配指导下，任务， 和技术框架，具体目标是：（1）检查用于ADRD的NLP驱动工具的可用性 临床试验资格预筛选，以及（2）评估所做资格预筛选的效率和准确性 临床研究人员使用NLP驱动的工具进行ADRD临床试验。拟议的目标是一致的 与卫生保健研究和质量机构（AHRQ）在支持研究方面的优先事项， 通过审查创新的保健市场办法，增加获得保健服务的机会和负担得起的费用 交付.拟议研究的结果有可能为成功的ADRD提供关键发现 临床研究招募，以加速知识发现和疾病修饰的开发 治疗ADRD。最后，R36论文奖将为博士预科生提供宝贵的机会 学生为她成为一名独立护理科学家的长期目标奠定了坚实的基础， 在老龄化人口的临床研究中利用信息学的专业知识。

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.