geneXwell: Multidimensional Omic Risk Models and Dynamic Visualizations to Drive Positive Change in Employee Behavioral Economics

员工生产力直接关系到员工的健康，为雇主提供了强大的经济激励 部署预防性健康计划。冠心病是最具挑战性和代价最高的慢性疾病之一。 动脉疾病(CAD)。大多数雇主花费总福利的很大一部分(40%-45%)来管理和 治疗与冠心病相关的症状和风险因素。每个CAD事件(心脏病发作、心绞痛)和相关 手术(支架、冠状动脉旁路移植术)花费雇主12.5万美元的直接医疗和生产成本。这些CAD活动 也是美国头号死因。自我保险的雇主，他们提供健康 覆盖美国的1亿个人，直接承担CAD的费用。因此，任何具有成本效益的方法 能够减少员工群体中的冠心病发病率，特别是通过早期干预 具有显著的社会效益和经济效益。 GeneXwell通过瞄准我们世界级数字预防性心脏病的交付提供了这一机会 为那些冠心病风险最高、最有可能从降脂治疗中受益的员工提供这一计划。作为一部分 在普通员工健康风险评估中，员工提供临床和基因组筛查样本 分析。标准的人口学和生物统计学风险因素与遗传风险估计相结合，结果 在每个员工的个性化CAD风险分值中。遗传风险的增加既改善了风险分层，也像 与标准临床指南相比，更重要的是确定了风险的性质和最有效的 干预措施。这一战略得到了我们在Scripps的联合创始人的研究的验证和支持 翻译研究所。在员工设置中，使用这种全面的风险建模来分层 员工汇集到风险级别，并运行分析以确定生活方式与治疗方式的成本与收益 针对每个风险级别的干预策略。然后通过直观的方式汇总和显示这些信息 可视化工具，允许员工评估预防行为和健康干预的好处。 动态可视化工具将实现协作、共享决策制定和可见性 利益相关者。收入将通过软件即服务和雇主的风险分担模式产生。 第一阶段的目标是将我们已建立的基线风险模型扩展到雇主提供的数据 健康设置，我们将开发一个原型员工可视化界面，并进行可用性研究。第一, 我们将建立在我们现有的、经过验证的多基因CAD评分模型基础上。我们将开发和部署CAD风险 根据遗传、人口统计和临床因素进行个性化评分，以生成个性化的CAD风险评分 员工。将开发一个降低风险的接口，将预防战略和预期健康结合起来 推动员工行为改变的好处。接下来，将开发一个原型员工移动平台 专注于跨多个域和源的数据同步，以及动态、直观 可视化工具，由行为科学专业知识开发和提供信息，用于指导复杂的行为健康 由员工做出决定。一旦原型平台在系统级集成并通过 验证和验证测试，它将被部署在与员工一起进行的可用性研究中，以验证可解释性。

Employee productivity is directly related to employee health, providing employers strong financial incentives to deploy preventative health programs. One of the most challenging & costly chronic conditions is coronary artery disease (CAD). Most employers spend a significant portion of overall benefits (40-45%) managing & treating symptoms and risk factors associated with CAD. Each CAD event (heart attack, angina) and related procedures (stents, CABG) costs the employer $125k in direct medial and productivity costs. These CAD events are also the number 1 cause of death in the United States. Self-insured employers, which provide health coverage to 100M individuals in the US, bear the costs of CAD directly. Therefore, any cost-effective approach able to reduce CAD incidence in employee populations, particularly through early interventions would have significant societal and economic benefits. geneXwell provides this opportunity by targeting the delivery of our world-class digital preventative cardiology program to those employees most at risk for CAD and most likely to benefit from lipid lowering therapy. As part of ordinary employee health risk assessment, employees provide screening samples for clinical and genomic analysis. Standard demographic and biometric risk factors are combined with a genetic risk estimate, resulting in a personalized CAD risk score per employee. The addition of genetic risk both improves risk stratification as compared to standard clinical guidelines and, more importantly, identifies the nature of risk and the most effective interventions. This strategy is validated and supported by the research of our co-founders at The Scripps Research Translational Institute. In the employee setting, this comprehensive risk modeling is used to stratify the employee pool into risk tiers, and analytics run to determine the cost vs benefit of lifestyle vs therapeutic intervention strategies for each risk tier. This information is then summarized and displayed via intuitive visualization tools that allow employees to evaluate the benefits of prevention behaviors and health interventions. Dynamic visualizations tools will allow collaboration, shared decision making and visibility across all stakeholders. Revenue will be generated through Software as a Service and risk share models to employers. Phase I will target the extension of our established baseline risk model to the data available in an employer health setting, we will develop a prototype employee visualization interface, and conduct a usability study. First, we will build on our existing, validated polygenic CAD scoring model. We will develop and deploy a CAD risk score personalized with genetic, demographic, and clinical factors to produce individualized CAD risk scores for employees. A risk reducer interface will be developed to integrate prevention strategies and anticipated health benefits to drive employee behavioral change. Next, a prototype employee mobile platform will be developed with focus on data synchronization across multiple domains and sources, as well as dynamic, intuitive visualization tools, developed and informed by behavioral science expertise, to guide complex behavioral health decision making by employees. Once the prototype platform has been integrated at the system level and passes verification and validation testing, it will be deployed in a usability study with employees to validate interpretability.