ASCEND: ApproacheS to CHC ImplEmeNtation of SDH Data Collection and Action

ASCEND：CHC 实施 SDH 数据收集和行动的方法

• 批准号: 9376920
• 负责人: RACHEL GOLD
• 金额: $ 70.32万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-25 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9376920
• 关键词: Address; Adopted; Adoption; Adult; Affect; Baseline Surveys; Caring; Child health care; Clinic; Clinical; Cluster randomized trial; Communities; Community Health Centers; Community Healthcare; Data; Data Collection; Diabetes Mellitus; Disease; Electronic Health Record; Evaluation; Funding; General Population; Glycosylated hemoglobin A; Guidelines; Health; Health Insurance; Health Personnel; Impact evaluation; Informatics; Institute of Medicine (U.S.); Intervention; Knowledge; Lipids; Measures; Medical; Medicare; Methods; National Institute of Diabetes and Digestive and Kidney Diseases; Obesity; Outcome; Patients; Pattern; Pilot Projects; Positioning Attribute; Prevalence; Prevention; Preventive care; Primary Health Care; Process; Provider; Public Health; Quality of Care; Randomized; Recommendation; Recruitment Activity; Reporting; Research; Resources; Risk; Risk Management; Social Work; Standardization; Testing; Training; United States Centers for Medicare and Medicaid Services; Variant; Work; base; blood pressure regulation; clinical practice; community health study; community intervention; data management; design; diabetes risk; electronic structure; formative assessment; health data; health information technology; implementation science; improved; multidisciplinary; obesity prevention; obesity risk; programs; safety net; screening; social health determinants; social integration; standard care; tool; treatment planning; uptake

PROJECT SUMMARY/ABSTRACT: Health risks, outcomes, and care quality for people with / at risk for diabetes mellitus (DM) are profoundly affected by non-clinical factors called ‘social determinants of health’ (SDH). Diverse public health leaders and initiatives – including the Institute of Medicine, the Office of the National Coordinator for Health Information Technology, the Medicare Access & CHIP Reauthorization Act of 2015, and the Centers for Medicare & Medicaid Services 2016 Quality Strategy – emphasize the importance of documenting patients’ SDH data in electronic health records (EHRs), and using this data to inform care. However, little is known about how to help primary care teams routinely collect and act on SDH data using EHR-based tools. This knowledge gap is particularly problematic for the community health centers (CHCs) serving our nation’s most vulnerable patients, whose DM prevalence and risk (notably, obesity rates) are higher than the general population’s, and whose health is particularly impacted by SDH. The proposed trial builds on an NIDDK-funded pilot study in which we developed a suite of EHR-based SDH data management tools for primary care CHCs. In June 2016, these tools went live in 440 CHCs that are located in 19 states, but share a centrally-managed EHR. Having demonstrated that ‘SDH data tools’ can be built for CHCs, we now propose to assess: whether and how pragmatic implementation strategies that support other types of practice change will also help CHC teams systematically identify and take action on the SDH-related needs of adult patients with / at risk for DM; and, the impact of doing so on DM risk management. We will do this as follows. Step 1: Evaluate current EHR-based SDH data collection in 440 CHCs; use those formative results to hone a set of approaches for helping CHCs routinely collect SDH data and integrate it into care plans. Step 2: Conduct a pragmatic, stepped-wedge, cluster-randomized trial. Thirty CHCs will be randomized to one of five 6-month wedges, with staggered timing for receiving the intervention: a scalable implementation support package including technical assistance, training, and six months of access to an ‘SDH Implementation Team’ that will tailor support to meet each CHC’s needs. Step 3: Conduct a realist evaluation of how the impact on: (i) integration of SDH data collection into workflows; (ii) integration of SDH data into care; and (iii) DM risk management (controlled BP, HbA1c, BMI, lipids, etc.; up- to-date recommended care). Per PAR-15-157, we will test implementation strategies that are pragmatic, replicable, delivered under routine conditions, use existing resources, and target standard care processes. Our multidisciplinary team includes experts in SDH, implementation science, informatics, and primary care transformation. Study deliverables will include scalable strategies; results will inform SDH data collection and action implementation guidelines and materials for use by CHCs and primary care providers nationwide.

项目摘要 /摘要：患有 /应有 /有风险的人的健康风险，结果和护理质量 糖尿病（DM）受到“卫生社会决定者”的非临床因素的深刻影响 （SDH）。多样化的公共卫生领导人和倡议 - 包括医学研究所，办公室 国家健康信息技术协调员，《医疗保险访问与芯片读者法》 2015年，以及Medicare＆Medicaid Services中心2016质量战略 - 强调进口 在电子健康记录（EHR）中记录患者的SDH数据，并使用这些数据为护理提供信息。 但是，关于如何帮助初级保健团队常规收集和采取行动SDH数据的知之甚少 基于EHR的工具。对于社区卫生中心（CHC），这种知识差距尤其有问题 为我们国家最脆弱的患者服务，其DM患病率和风险（尤其是肥胖率）是 高于普通人群，其健康尤其受SDH的影响。拟议的审判 建立在NIDDK资助的试点研究的基础上，我们开发了一套基于EHR的SDH数据管理 初级保健CHC的工具。 2016年6月，这些工具在19个州的440个CHC中进行了直播， 但是共享一个由中央管理的EHR。证明可以为CHC构建“ SDH数据工具”后，我们 现在提出评估的建议：是否以及如何支持其他人的实施策略 练习类型更改还将帮助CHC团队系统地识别并采取行动 DM风险 /有风险的成年患者的SDH相关需求；而且，这样做对DM风险的影响 管理。我们将按照以下方式执行此操作。步骤1：评估440中基于EHR的当前基于EHR的SDH数据收集 CHC；使用这些形成性结果来磨练一组方法来帮助CHC通常收集SDH数据 并将其整合到护理计划中。步骤2：进行务实的，阶梯式的，群集随机试验。 30个CHC将被随机分为五个6个月的楔子之一，并带有交错的时机 干预：可扩展的实施支持软件包，包括技术援助，培训和六个 几个月来，可以访问“ SDH实施团队”，该团队将量身定制支持以满足每个CHC的需求。步 3：对如何影响：（i）将SDH数据收集集成到工作流程中； （ii） 将SDH数据集成到护理中； （iii）DM风险管理（受控BP，HBA1C，BMI，脂质等； UP- 迄今推荐的护理）。根据15-157杆，我们将测试务实的实施策略， 可复制，在常规条件下交付，使用现有资源和目标标准护理过程。 我们的多学科团队包括SDH，实施科学，信息信息和初级保健专家 转型。研究可交付成果将包括可扩展的策略；结果将为SDH数据收集提供信息，并且 CHC和全国初级保健提供者使用的行动实施指南和材料。