Social Networks in Medical Homes and Impact on Patient Care and Outcomes

医疗之家的社交网络及其对患者护理和结果的影响

• 批准号: 10326793
• 负责人: Lusine Poghosyan
• 金额: $ 35.95万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10326793
• 关键词: Social; Networks; Medical; Homes; Impact

The Patient-Centered Medical Home (PCMH) model aims to address primary care challenges such as poor access and quality and rising costs by delivering team-based care, particularly for chronic diseases. Yet little is known about the composition of effective teams to achieve best patient outcomes. Furthermore, how team members communicate, share advice, or help to deliver care or how the resulting social structures (i.e., social networks) affect quality and outcomes has not been studied. Our innovative mixed-methods study will fill this gap. We will combine analysis of team configurations and social networks in PCMH practices with assessment of quality of care and patient outcomes to identify team best practices. We will also collect qualitative data to assess the underlying teamwork dynamics not captured quantitatively. The specific aims are to: 1. Identify factors determining team configurations and the formation of social networks in primary care practices adopting the PCMH model. 2. Investigate how team configurations and social networks impact quality of care and patient outcomes for five chronic conditions. 3. Identify barriers, facilitators, and characteristics of teamwork for teams with different configurations, social networks, and performance. Practices (n=24) with the PCMH model at NewYork-Presbyterian Hospital/Columbia University and Weill Cornell Medical Centers and the University of Pittsburgh Medical Center will participate. We will recruit team members including clinicians and staff (n=1,437) through online surveys who will identify their team members from the clinic's roster and report with who they communicate, share advice and/or support, and trust or approach to solve problems. We expect an 80% response rate (n=1,150). We will obtain patient data on quality of care and outcomes for diabetes, asthma, hypertension, cardiovascular disease, and chronic obstructive pulmonary disease and merge it with survey data. ORA* and R software will be used for data analysis. We will map team configuration and social networks, visualize them, and compute network metrics. We will then build Exponential Random Graph Models to predict factors explaining the observed networks and multilevel models to assess the impact of network variables on quality of care and patient outcomes. Based on Aim 1 findings, we will recruit participants who are highly- (n=~20) and poorly- (n=~20) connected to their team members in social networks. Based on Aim 2 findings, we will recruit participants from high- (n=~20) and low- (n=~20) performing teams. We will conduct individual face- to-face interviews with them using an interview guide. Interviews will be audio-taped and transcribed, and data will undergo content analysis. Multiple researchers will code the data and identify themes. Quantitative and qualitative findings will be triangulated. This study has the potential to show how to facilitate teamwork and identify the most effective team attributes to assure best quality of care and outcomes particularly for patients with chronic diseases (AHRQ's priority population). This application is in response to the Special Emphasis Notice (SEN) NOT-HS-16-011 on AHRQ's interest in applications related to innovative primary care research.

以患者为中心的医疗之家（PCMH）模型旨在应对诸如差的初级保健挑战 通过提供基于团队的护理，尤其是针对慢性疾病，访问，质量和成本上升。但是几乎没有 知道有效团队的组成，以实现最佳的患者结果。此外，如何团队 成员交流，分享建议或帮助提供护理或如何产生的社会结构（即社会结构） 网络）影响质量和结果尚未研究。我们创新的混合方法研究将填补这一点 差距。我们将在PCMH实践中将团队配置和社交网络的分析与评估结合 护理质量和患者成果，以确定团队最佳实践。我们还将收集定性数据 评估未定量捕获的基本团队工作动态。具体目的是：1。识别 确定团队配置和基本护理实践中社交网络形成的因素 PCMH模型。 2。调查团队配置和社交网络如何影响护理质量和 五个慢性病的患者预后。 3。确定障碍，促进者和团队合作的特征 具有不同配置，社交网络和性能的团队。 PCMH模型的实践（n = 24） 在纽约 - 普雷斯特里亚医院/哥伦比亚大学和威尔·康奈尔医学中心和大学 匹兹堡医疗中心将参加。我们将招募包括临床医生和员工在内的团队成员（n = 1,437） 通过在线调查，他们将从诊所的阵容中识别其团队成员并与他们报告谁 交流，分享建议和/或支持，以及解决问题的信任或方法。我们预计80％ 响应率（n = 1,150）。我们将获得有关糖尿病，哮喘，哮喘和结果的患者数据 高血压，心血管疾病和慢性阻塞性肺疾病，并将其与调查合并 数据。 ORA*和R软件将用于数据分析。我们将绘制团队配置和社交网络， 可视化它们，并计算网络指标。然后，我们将构建指数随机图模型以预测 解释观察到的网络和多级模型的因素，以评估网络变量对 护理质量和患者结果。根据AIM 1的发现，我们将招募高度的参与者 （n = 〜20）且差 - （n = 〜20）与其团队成员在社交网络中相连。根据AIM 2的发现，我们 将招募高高（n = 〜20）和低 - （n = 〜20）的参与者。我们将进行个人脸 - 面对他们的面试访谈指南。访谈将是音频和转录的，数据 将进行内容分析。多个研究人员将编码数据并识别主题。定量和 定性发现将进行三角测量。这项研究有可能展示如何促进团队合作和 确定最有效的团队属性，以确保最佳护理质量和成果，尤其是对患者 患有慢性疾病（AHRQ的优先人群）。此应用是对特殊重点的回应 通知（SEN）关于AHRQ对与创新初级保健研究有关的应用的兴趣的非HS-16-011。