Bring it Down San Francisco: Harnessing cloud-based technology to improve hypertension management in a low-income underserved population

打倒旧金山：利用基于云的技术改善低收入服务不足人群的高血压管理

• 批准号: 9904746
• 负责人: Valy Fontil
• 金额: $ 17.74万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-10 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904746
• 关键词: Adoption; Biometry; California; Cardiovascular system; Caring; Chronic Disease; Clinic; Clinic Visits; Clinical; Computers; Data; Data Science; Decision Analysis; Disease Management; Effectiveness; Electronic Health Record; Emerging Technologies; Epidemiologist; Evaluation; Evidence based intervention; Foundations; Frequencies; Funding; Future; Generations; Goals; Head; Health; Health Services; Health Services Research; Health behavior change; Health system; Healthcare; Healthcare Systems; Hypertension; Institution; Internal Medicine; Internet; Intervention; K-Series Research Career Programs; Lead; Low Income Population; Low income; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Minority; Modeling; Online Systems; Outcome; Patient Care; Patients; Physicians; Play; Population; Primary Care Physician; Primary Health Care; Process; Provider; Race; Randomized Controlled Trials; Recommendation; Research; Research Personnel; Resources; Role; San Francisco; Scientist; Statistical Data Interpretation; System; Techniques; Technology; Testing; Training; Treatment Protocols; Underserved Population; United States National Institutes of Health; Universities; Visit; Vulnerable Populations; Work; base; blood pressure regulation; cardiovascular disorder epidemiology; cardiovascular risk factor; career; clinical decision-making; cloud based; comparative; design; health care settings; health data; health difference; health disparity; high risk population; hypertension control; hypertension treatment; implementation research; improved; invention; longitudinal analysis; medication compliance; models and simulation; multidisciplinary; new technology; patient oriented; patient population; population health; post intervention; professor; programs; racial difference; racial disparity; racial health disparity; safety net; service intervention; shared decision making; skills; trend; usability

PROJECT SUMMARY/ABSTRACT This an application for a K23 Mentored Patient-Oriented Career Development Award for Dr. Valy Fontil, an Assistant Adjunct Professor in General Internal Medicine at the University of California San Francisco who is establishing himself as a young investigator in implementation research of health systems-based strategies to improve hypertension control in underserved, high-risk populations. This K23 award will provide him with the necessary support evaluation of a hypertension management invention in 12 safety-net clinics and test a novel technology-enabled intervention that engages patients and enables shared decision-making between patients and providers to optimize visit frequency, treatment intensification, and medication adherence for treatment of hypertension. To achieve these goals, Dr. Fontil has assembled a multi-disciplinary mentoring team comprised of a primary mentor, Dr. Kirsten Bibbins-Doming, a renowned expert in cardiovascular epidemiology, health disparities research, and simulation modeling, and two co-mentors: Dr. Mark Pletcher, renowned cardiovascular epidemiologist with added expertise in clinical decision-making, decision analysis, and use of emerging technology for improving health; and Dr. Charles McCulloch, Head of the Division of Biostatistics at UCSF and expert in advanced statistical analysis of longitudinal data. Safety-net healthcare institutions, which care for our highest-risk populations, must play a pivotal role in achieving national priorities for improved BP control and reducing HTN disparities. Therefore, it is essential to develop and test practical solutions that these healthcare systems can employ within their resource constraints. Dr. Fontil will build on findings from his previous work in simulation modeling and real-world pilot intervention to focus on optimizing two key processes of care (visit frequency, treatment intensification, and medication adherence) that can improve BP control to upward of 80%. First, he will evaluate race-specific effects of a health system intervention in safety net clinics (Aims 1&2) on improving these processes. Then he will adapt and test a technology-enabled intervention for feasibility of improving these processes in patients at safety-net clinics (Aim 3). The proposed research will provide the foundation and additional information needed to design a randomized controlled trial of a technology-enabled intervention to improve overall BP control and reduce racial disparities in BP control across a consortium of safety-net healthcare systems. This will form the basis of my future R01 proposal. Through a focused program of mentored training and coursework, the candidate will gain advanced skills and expertise in (1) training in using and analyzing electronic health record data for health services research, (2) advanced techniques in computer microsimulation modeling, and (3) foundational concepts and skills at the intersection of data science, technology and healthcare.

项目总结/摘要 这是一个K23指导的以病人为导向的职业发展奖的申请，Valy Fontil博士， 他是加州旧金山弗朗西斯科的普通内科助理兼职教授， 使自己成为一名年轻的研究人员，从事基于卫生系统的战略的执行研究， 改善服务不足、高危人群的高血压控制。这个K23奖将为他提供 在12个安全网诊所对高血压管理发明进行必要的支持评估，并测试一种新的 技术支持的干预，使患者参与并在患者之间共享决策 和提供者，以优化访问频率，治疗强化和药物治疗的依从性， 高血压为了实现这些目标，Fontil博士组建了一个多学科的指导团队， Kirsten Bibbins-Doming博士是一位心血管流行病学、健康、 差异研究和模拟建模，以及两位共同导师：Mark Pletcher博士，著名的 心血管流行病学家，在临床决策，决策分析和使用 改善健康的新兴技术;和查尔斯·麦卡洛克博士，生物统计司司长， 加州大学旧金山分校和专家在先进的纵向数据统计分析。 照顾我们最高风险人群的安全网医疗保健机构必须在以下方面发挥关键作用： 实现改善BP控制和减少HTN差异的国家优先事项。因此必须 开发和测试这些医疗保健系统可以在其资源范围内使用的实用解决方案 约束Fontil博士将建立在他以前在模拟建模和现实世界试点工作的基础上 干预，重点是优化两个关键的护理过程（访问频率，治疗强化， 药物依从性），可以提高血压控制到80%以上。首先，他将评估种族特异性 卫生系统在安全网诊所的干预措施对改善这些进程的影响（目标1和2）。然后他 将调整和测试技术支持的干预措施，以改善患者的这些过程的可行性， 安全网诊所（目标3）。拟议的研究将提供所需的基础和额外信息 设计一项技术支持干预的随机对照试验，以改善整体血压控制， 减少安全网医疗保健系统联盟中BP控制的种族差异。这将形成 未来的R 01计划。 通过有针对性的指导培训和课程计划，候选人将获得先进的技能， 专业知识（1）培训使用和分析电子健康记录数据进行卫生服务研究，（2） 计算机微观模拟建模的先进技术，以及（3）在 数据科学、技术和医疗保健的交叉点。