PhendoPHL:A Data-Science Enabled Personal Health Library to Manage Endometriosis

项目总结 子宫内膜异位症是一种慢性疾病，估计有10%的育龄妇女会受到影响。它有一个 对生活质量和生产力造成很大负担，以及与妇女生活在一起的自我管理需求 这种疾病是多种多样的。该项目旨在设计、开发和评估支持数据科学的个人 名为PHODOPHL的健康图书馆，以支持与艾滋病相关的妇女的自我管理需求 子宫内膜异位症。以自主决策理论为基础，以用户为中心的设计方法 PhdoPHL将通过集成的 临床和自我跟踪数据，识别时间个性化模式并与人群正常值进行比较 通过新的数据科学方法，并为共享决策提供可操作的数据可视化 在患者与提供者见面时做出的。PhdoPHL建立在我们现有的新型信息学工作的基础上 治疗子宫内膜异位症的方法，以及我们研究团队在设计和评估方面的丰富经验 新的信息学干预。这项拟议的工作还填补了个人健康信息学的一个研究空白： 开发和验证新的计算方法，以识别个性化和人口- 基于临床和自我监测数据的模式；这两种类型的数据对成功的自我监测至关重要 管理和计算方面的挑战，因为它们具有时间性、异构性、 而且很稀疏。使用混合方法评估研究(标准化调查、日志文件分析、关键 事件技术访谈、焦点小组)，我们将研究PhdoPHL的可用性，评估影响因素 对用户参与度和对自主决策和共享决策的感知影响至关重要，以及 对妇女健康中的其他生殖慢性病的概括性。

PROJECT SUMMARY Endometriosis is a chronic condition which is estimated to affect 10% of women in reproductive age. It has a very high burden on quality of life and productivity, and the self-management needs of women living with the disease are multiple. This project aims to design, develop, and evaluate a data-science enabled personal health library called PhendoPHL to support the self-management needs of women living with endometriosis. Grounded in self-determination theory, and informed by user-centered design methods PhendoPHL will enable exploration of health patterns through interactive visualizations of integrated clinical and self-tracked data, identify temporal personalized patterns and comparison to population norms through novel data-science methods, and provide actionable visualizations of data for shared decision making during patient-provider encounters. PhendoPHL builds on our existing work in novel informatics methods for endometriosis, and the extensive experience of our research team in designing and evaluating novel informatics interventions. The proposed work also fills a research gap in personal health informatics: the development and validation of novel computational methods to identify personalized and population- based patterns in clinical and self-monitoring data; both types of data which are critical to successful self- management and challenging from a computational standpoint because they are temporal, heterogeneous, and sparse. Using a mixed-methods evaluation study (standardized surveys, logfile analysis, Critical Incident Technique interviews, focus groups), we will study PhendoPHL’s usability, assess the factors critical to user engagement and perceived impact on self-determination and shared decision making, and the generalizability to other reproductive chronic conditions in women’s health.