Integrative Data Science Approach to Advance Care Coordination of ADRD by Primary Care Providers

摘要 患有阿尔茨海默病和相关痴呆症(ADRD)的老年人需要许多专家的护理 以及临床团队来管理与ADRD相关的症状和其他共病。大多数患者都患有 ADRD的医疗保健由非专家管理，他们往往缺乏时间、信心和专业知识来 管理持续的ADRD需求，这导致基于转诊的重大护理往往缺乏 协调。通过以下方式支持初级保健提供者对ADRD患者的持续护理管理 促进临床团队之间有意识地组织护理活动和信息共享是至关重要的 有机会限制意外差距并确保ADRD患者获得高质量的多学科 为长期健康提供必要的护理。几乎没有解决办法来衡量和确定护理协调方面的差距。 目前的方法主要依靠单一付款人索赔数据来评估患者之间的共享关系 提供者，忽视提供改善当地医疗保健服务所需的细粒度洞察力。施药 对电子病历使用和通信数据的高级统计建模将提供对医疗保健的重要洞察 准确建模和优化基于转诊的护理协调所需的交付模式。 在提议的项目中，我将应用创新的知识表示和机器学习来改进 基于转诊的护理协调，通过开发监测协调活动和 推荐可行的改进机会。在一个多学科导师团队的指导下， 我将接受培训，以扩大我在医疗保健服务方面的知识，以促进健康老龄化，进一步我的 了解最先进的机器学习技术，并将加深对 研究复杂社会技术系统的定量方法。我将应用此培训来解决 与前两个目标中基于转诊的临床团队的形成相关的知识差距：(1)模型和 确定治疗ADRD患者的医疗保健提供者团队之间的协作模式 改进医疗保健服务；以及(2)将自然语言处理应用于通过患者门户发送的消息 了解患者和护理者的互动如何影响护理模式。在《目标3》中，我将把洞察力和 协作网络从前两个目标出发，旨在开发可解释的机器学习模型，以确定最优 护理协调的模式。我将使用这些最佳护理协调模式来强调导致 患者治疗路径中的偏差，并确定可操作的改进步骤。 这一职业发展奖将提供必要的严格培训和指导，以成为一个 独立首席调查员。这项研究将受益于一位具有很强背景的私人投资 信息科学、知识表示和协作分析。我已经召集了一支杰出的 多学科导师团队，在拟议项目的所有领域拥有广泛的专业知识，并将 范德比尔特大学医疗中心优越的环境给予您特殊的支持。

ABSTRACT Older adults with Alzheimer’s disease and related dementias (ADRD) require care from numerous specialists and clinical teams to manage ADRD-related symptoms and other comorbidities. The majority of patients with ADRD have their healthcare managed by non-specialists who often lack the time, confidence, and expertise to manage ongoing ADRD needs, which leads to significant referral-based care that often suffers from a lack of coordination. Supporting primary care providers in their ongoing management of care for patients with ADRD by promoting deliberate organization of care activities and information sharing among clinical teams is a critical opportunity to limit unintended gaps and ensure that patients with ADRD receive the high-quality multidisciplinary care necessary for long-term wellbeing. Few solutions exist to measure and identify gaps in care coordination. Current approaches primarily rely on single payor claims data to evaluate patient sharing relationships between providers, which neglects to provide granular insight necessary to improve local healthcare delivery. Applying advanced statistical modeling to EHR usage and communication data will provide critical insight into healthcare delivery patterns necessary to accurately model and optimize referral-based care coordination. In the proposed project, I will apply innovative knowledge representation and machine learning to improve referral-based care coordination by developing intelligent approaches that monitor coordination activities and recommend actionable opportunities for improvement. Under the guidance of a multidisciplinary team of mentors, I will receive training to expand my knowledge in healthcare delivery to promote healthy aging, further my knowledge of state-of-the-art machine learning techniques, and will develop a deeper understanding of quantitative approaches to investigate complex sociotechnical systems. I will apply this training to address knowledge gaps related to the formation of referral-based clinical teams in the first two aims: (1) model and identify patterns of collaboration among healthcare providers teams treating patients with ADRD that contribute to improved healthcare delivery; and (2) apply natural language processing to messages sent via patient portal understand how patient and caregiver interactions influence care patterns. In aim 3, I will combine insights and collaboration networks from the first two aims to develop explainable machine learning models to identify optimal patterns of care coordination. I will use these optimal care coordination patterns to highlight features that cause deviation in a patient’s treatment pathway and identify actionable steps for improvement. This career development award will provide the rigorous training and mentorship necessary to become a fully independent principal investigator. The research will benefit from a PI who has a strong background in information science, knowledge representation, and collaboration analytics. I have assembled an outstanding multidisciplinary team of mentors with extensive expertise across all areas of the proposed project and will receive exceptional support from an outstanding environment at Vanderbilt University Medical Center.