Care System Analytics to Support Primary Care Patients with Complex Medical and Social Needs

护理系统分析支持具有复杂医疗和社会需求的初级保健患者

• 批准号: 10259671
• 负责人: RICHARD W GRANT
• 金额: $ 62.93万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259671
• 关键词: Care; System; Analytics; Support; Primary

Project Summary/Abstract One third of Americans have multiple chronic conditions (MCC), including 80% of individuals age 65 or older. Many of these individuals do not experience the full benefit of evidence-based medicine due to socially determined barriers to effective care that contribute to health inequity and poorer health outcomes. These potentially actionable barriers are not systematically incorporated into existing primary care models and may be underrecognized and undermanaged in the primary care setting. Research is needed to develop tools that can systematically and iteratively identify patients with actionable social barriers and link them to the primary care team members best suited to address and overcome these barriers. In this proposal, we seek to address current gaps in knowledge by using advanced analytic techniques to predict patients at high risk for having socially-determined care barriers (Aim 1), building an MCC Social Needs EHR-linked dashboard to enable primary care teams to iteratively prioritize and manage adults with MCC complicated by actionable, socially determined barriers to health (Aim 2), and evaluating the acceptability and use of this dashboard in 3 low- income communities (Richmond, CA; Rainier Valley, WA; and Aurora, CO) served by 3 different Kaiser Permanente organizations (Aim 3). Our team structure is designed to support robust collaboration between our team of scientific researchers embedded within health systems and stakeholder partners that include clinical and operational health system leaders, community-based leaders, primary care team providers, and patients and their caregivers. We will use transparent advanced analytic approaches, user centered design methods, and robust implementation evaluation practices to ensure that our dashboard tool can be effectively adapted and replicated to different clinical contexts and our results can guide subsequent implementation decisions.

项目总结/文摘