ToolBox Detect: Low Cost Detection of Cognitive Decline in Primary Care Settings

项目摘要 我们的目标是广泛实施和评估以用户为中心的、可扩展的电子健康记录 (EHR)- 在不同的初级保健环境中常规检测认知能力下降的相关策略。 认知障碍在65岁及以上的成年人中最为普遍，但只有不到一半的病例被发现和/或 在初级保健机构诊断。现在越来越多的人认为，早期发现对 优化护理规划、持续独立性、慢性病管理和适当的护理人员 参与。2011年，Medicare启动了一项承保的年度健康访视（AWV），其中包括认知 评估以检测损伤、阿尔茨海默病和相关痴呆症（ADRD）。虽然这已经提出了 病例发现的新机会，AWV的实施一直是可变的，包括认知功能如何 评估。临床医生可能依赖于更基本的，基于纸张的，面试官管理的测试， 精确且对临床工作流程来说更麻烦-影响早期检测策略的保真度。此外，本发明还 许多做法缺乏一个明确的协议，转介时，损害是确定的，和一个程序， 参与和建立护理目标。迫切需要实用、可持续、可扩展的战略来帮助 处于医疗保健第一线的初级保健提供者通常将认知功能作为AWV的一部分进行评估 (or当怀疑有认知障碍时），确定问题并制定转诊和护理协议 管理这对于资源有限的临床环境尤其如此，例如联邦合格卫生 中心（CNOHC）照顾更脆弱的患者群体。 西北大学开发并继续创新NIH神经和神经功能评估工具， Behavioral Function®.自2017年以来，我们的团队与初级保健实践密切合作，制定了一个简短的， 技术支持的，自我管理的，EHR相关的认知评估来自美国国立卫生研究院。我们也 设计了一个详细的协议，为它的使用，以及如何我们的测试结果，称为工具箱检测，可以告知病人 在乎我们建议进行一项大规模的初级保健实践随机试验， 将ToolboxDetect作为AWV的标准护理进行评估，并链接到EHR（Epic）。多样性，学术性和 社区环境也包括在内，以优化今后的传播工作。 我们的主要目的是评估ToolboxDetect的有效性，与增强的常规护理相比， 及时发现认知能力下降及其护理管理。我们的第二个目标是：2）传播和 在一个大型联邦卫生中心网络中实施ToolboxDetect，并评估其可行性， 使用的可接受性; 3）调查ToolboxDetect的保真度，并识别任何患者、护理人员、医疗保健人员 提供商和/或系统障碍，以实现其最佳的，持续的实施;和4）确定相关的成本 从初级保健的角度实施ToolboxDetect。

PROJECT SUMMARY Our objective is to widely implement and evaluate a user-centered, scalable, electronic health record (EHR) - linked strategy for the routine detection of cognitive decline among diverse primary care settings. Cognitive impairment is most prevalent among adults 65 and older, yet less than half of cases are detected and/or diagnosed in primary care settings. It is now increasingly accepted that early detection is critically important to optimize care planning, sustained independence, management of chronic conditions and appropriate caregiver involvement. In 2011, Medicare initiated a covered, Annual Wellness Visit (AWV) that includes a cognitive assessment to detect impairment, Alzheimer’s disease and related dementias (ADRD). While this has presented new opportunity for case finding, implementation of AWVs has been variable, including how cognitive function is assessed. Clinicians may rely on more basic, paper-based, interviewer-administered tests that may be less precise and more cumbersome to clinical workflow – affecting the fidelity of an early detection strategy. Further, many practices lack a clear protocol for referral when impairment is determined, and a process for family involvement and establishing care goals. Practical, sustainable, scalable strategies are urgently needed to help primary care providers who are on the frontlines of healthcare routinely assess cognitive function as part of AWVs (or whenever a cognitive impairment is suspected), identify concerns and have a protocol for referrals and care management. This is especially true for resource-constrained clinical settings, such as Federally Qualified Health Centers (FQHCs) caring for more vulnerable patient populations. Northwestern developed and continues to innovate the NIH Toolbox for the Assessment of Neurological and Behavioral Function®. Since 2017, our team has worked closely with primary care practices to develop a brief, technology-enabled, self-administered, EHR-linked cognitive assessment derived from the NIH Toolbox. We also devised a detailed protocol for its use and how results of our test, known as ToolboxDetect, can inform patient care. We propose a large-scale, primary care practice-randomized trial to implement and comprehensively evaluate ToolboxDetect as a standard of care with AWVs, linked to an EHR (Epic). Diverse, academic and community settings are included to optimize future dissemination efforts. Our primary aim is to evaluate the effectiveness of ToolboxDetect, compared to enhanced usual care, to promote timely detection of cognitive decline and its care management. Our secondary aims are to: 2) Disseminate and implement ToolboxDetect among a large Federally Qualified Health Center Network and assess its feasibility and acceptability for use; 3) Investigate the fidelity of ToolboxDetect, and identify any patient, caregiver, healthcare provider and/or system barriers to its optimal, sustained implementation; and 4) Determine costs associated with implementing ToolboxDetect from a primary care perspective.