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

综合数据科学方法促进初级保健提供者对 ADRD 的护理协调

• 批准号: 10722568
• 负责人: Bryan Steitz
• 金额: $ 11.93万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722568
• 关键词: Abnormal coordination; Academic Medical Centers; Address; Aging; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Area; Caregiver support; Caregivers; Caring; Classification; Client satisfaction; Clinical; Clinical Management; Collaborations; Communication; Complex; Data; Data Science; Diagnosis; Disease; Early Diagnosis; Elderly; Electronic Health Record; Ensure; Environment; Failure; Goals; Health Personnel; Health Services Research; Healthcare; Information Sciences; Intelligence; K-Series Research Career Programs; Knowledge; Learning; Machine Learning; Measures; Medical Care Team; Mentors; Mentorship; Mining; Modeling; Monitor; Natural Language Processing; Nature; Pathway Analysis; Pathway interactions; Patient Care; Patient Care Management; Patients; Pattern; Patterns of Care; Personal Satisfaction; Positioning Attribute; Primary Care; Principal Investigator; Process; Provider; Quality of life; Randomized, Controlled Trials; Recommendation; Research; Research Personnel; Resources; Social Network; Specialist; Statistical Methods; Statistical Models; Structure; Suggestion; Symptoms; System; Taxonomy; Techniques; Time; Training; Work; acute care; care coordination; care fragmentation; career; collaborative care; comorbidity; data communication; dementia care; health care delivery; health care service; health management; healthy aging; improved; improved outcome; information organization; innovation; insight; longitudinal care; machine learning model; medical specialties; member; multidisciplinary; neglect; novel; patient portal; primary care provider; programs; team-based care; treatment and outcome; trend

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.

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 和临床团队来管理ADRD相关症状和其他合并症。大多数患者 ADRD的医疗保健由非专家管理，他们通常缺乏时间，信心和专业知识， 管理正在进行的ADRD需求，这导致严重的基于家庭的护理，往往缺乏 协同支持初级保健提供者对ADRD患者的持续护理管理， 促进精心组织的护理活动和临床团队之间的信息共享是一个关键 有机会限制非预期的差距，并确保ADRD患者接受高质量的多学科 长期健康所需的护理。几乎没有解决方案来衡量和确定护理协调方面的差距。 目前的方法主要依赖于单一支付者索赔数据来评估患者之间的共享关系。 提供者，忽视了提供改善当地医疗服务所需的粒度洞察力。应用 对EHR使用和通信数据的高级统计建模将提供对医疗保健的关键洞察 提供必要的模式，以准确地建模和优化基于社区的护理协调。 在建议的项目中，我将应用创新的知识表示和机器学习来提高 通过开发监测协调活动的智能方法， 建议可行的改进机会。在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，促进健康老龄化， 了解最先进的机器学习技术，并将更深入地了解 用定量方法研究复杂的社会技术系统。我将应用此培训来解决 知识差距有关的医院为基础的临床团队的形成在前两个目标：（1）模式和 确定治疗ADRD患者的医疗保健提供者团队之间的合作模式， 改善医疗保健服务;（2）将自然语言处理应用于通过患者门户发送的消息 了解患者和护理人员的互动如何影响护理模式。在目标3中，我将结合联合收割机的见解， 前两者的协作网络旨在开发可解释的机器学习模型，以确定最佳的 护理协调模式。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，并确定可行的改进步骤。 这个职业发展奖将提供严格的培训和必要的指导，成为一个全面的 独立首席调查员这项研究将受益于PI谁拥有强大的背景， 信息科学、知识表示和协作分析。我召集了一个杰出的 多学科的导师团队，在拟议项目的所有领域都具有广泛的专业知识，并将 从范德比尔特大学医学中心的出色环境中获得卓越的支持。