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.

抽象的 患有阿尔茨海默病和相关痴呆症 (ADRD) 的老年人需要众多专家的护理 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 管理持续的 ADRD 需求，这会导致大量基于转诊的护理，而这种护理往往因缺乏 协调。支持初级保健提供者对 ADRD 患者进行持续护理管理 促进有意识地组织护理活动和临床团队之间的信息共享至关重要 有机会限制意外差距并确保 ADRD 患者接受高质量的多学科治疗 长期健康所必需的护理。很少有解决方案可以衡量和识别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 提供商，忽视了提供改善当地医疗保健服务所需的细致洞察。正在申请 EHR 使用和通信数据的高级统计模型将为医疗保健提供重要的见解 准确建模和优化基于转诊的护理协调所需的交付模式。 在拟议的项目中，我将应用创新的知识表示和机器学习来改进 通过开发智能方法来监控协调活动和基于转诊的护理协调 推荐可行的改进机会。在多学科导师团队的指导下， 我将接受培训，以扩展我在医疗保健方面的知识，以促进健康老龄化，进一步提高我的能力 最先进的机器学习技术的知识，并将加深对 研究复杂社会技术系统的定量方法。我将应用这次培训来解决 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合作模式，这些模式有助于 改善医疗保健服务； (2) 对通过患者门户发送的消息应用自然语言处理 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，我将结合见解和 前两个的协作网络旨在开发可解释的机器学习模型来识别最佳的 护理协调模式。我将使用这些最佳护理协调模式来突出导致 患者治疗途径的偏差并确定可行的改进步骤。 该职业发展奖将提供成为全面发展所需的严格培训和指导。 独立首席研究员。该研究将受益于具有强大背景的 PI 信息科学、知识表示和协作分析。我集结了一支优秀的 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 获得范德比尔特大学医学中心卓越环境的特殊支持。