Sociomarkers to Predict Asthma Control and Emergency Room Visits (SPACER)

预测哮喘控制和急诊室就诊的社会标记 (SPACER)

基本信息

批准号：
10328904
负责人：
Alon Peltz
金额：
$ 16.93万
依托单位：
HARVARD PILGRIM HEALTH CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-15 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10328904
关键词：
Adult Asthma Award Caring Child Classification Clinical Computer Models Continuity of Patient Care Data Data Set Demographic Factors Development Early identification Emergency department visit Enrollment Environment Environmental Risk Factor Evidence based intervention Face Family Financial Hardship Foundations Future Goals Health Insurance Health Services Healthcare Incentives Individual Intervention Investments K-Series Research Career Programs Learning Link Low income Machine Learning Measures Mediating Medicaid Medicine Mentors Methods Modality Modeling Morbidity - disease rate Outcome Patient Self-Report Patients Persons Pharmaceutical Preparations Physicians Population Positioning Attribute Privatization Public Policy Research Research Personnel Resources Risk Risk Factors Severity of illness Socioeconomic Factors Surveys Time asthma exacerbation base care seeking clinical risk cost disparities in morbidity experience food insecurity health plan health record high risk housing instability improved machine learning model medical schools medication compliance network models predictive modeling prevent programs random forest relative cost risk prediction risk prediction model skills social socioeconomic disparity

项目摘要

PROJECT ABSTRACT Asthma impacts more than 25 million adults and children in the U.S. with high associated morbidity and socioeconomic disparities in outcomes. Because effective medications are available to treat and prevent exacerbations of asthma and evidence-based interventions exist to mitigate the impact of harmful socioeconomic factors, early identification of those at highest risk is crucial. However, efforts to predict future exacerbations of asthma have yielded modest results with infrequent inclusion of comprehensive information on social hardships, such as food insecurity and housing instability, or financial hardships, such as difficulty affording the costs of controller medications which is particularly relevant for those with private health insurance. Identifying social and financial hardships requires broad-based screenings which are resource intensive, difficult to implement in clinical settings and often incomplete or limited to care seeking populations. Further, few asthma risk prediction modalities incorporate time-variable (temporal) data on important social, clinical, and environmental factors. Machine learning, an advanced computational approach to risk prediction, has great potential to improve upon conventional approaches to risk prediction of asthma exacerbations through indirect estimation of social hardships and inclusion of temporal risk factors. Implementation of enhanced asthma risk-prediction models in a health plan setting offers distinct advantages due to existing investments in asthma care management and access to timely claims data across the full care continuum. Accordingly, the aims of the SPACER study (Sociomarkers to Predict Asthma Control and Emergency Room visits) are 1) To describe social and financial hardships in privately insured adults and children with asthma, and association with medication adherence and exacerbations, 2) To indirectly estimate self-reported social and financial hardships using routinely collected health plan and spatial data, and 3) To develop and validate a machine learning network model, incorporating temporal sociomarker, clinical, and environmental data, to predict asthma exacerbations in a health plan setting. The research leverages the unique research environment of the Department of Population Medicine, an academic research department of Harvard Medical School, situated in a regional non-profit health plan, Harvard Pilgrim Health Care. The mentored career development award will support Dr. Alon Peltz, a physician and health services researcher, in developing expertise in machine learning modeling and use of social data to improve prediction of adverse clinical outcomes.

项目摘要哮喘在美国影响超过2500万成人和儿童，其发病率高结局中的社会经济差异。因为有效的药物可用于治疗和预防存在哮喘和基于证据的干预措施的加剧，以减轻有害的影响社会经济因素，对处于最高风险的人的早期识别至关重要。但是，预测未来的努力哮喘的加重已经产生了适度的结果，很少包含全面信息关于饮食不安全感和住房不稳定或经济困难，例如困难，例如困难提供控制器药物的成本，这与私人健康的人特别相关保险。确定社会和财务困难需要广泛的筛查，这是资源密集，难以在临床环境中实施，并且通常不完整或不限于寻求人群。此外，很少有哮喘风险预测方式包含有关重要社会的时间变化（时间）数据临床和环境因素。机器学习，一种先进的风险预测计算方法，有很大的潜力可以改善常规方法，以预测哮喘恶化的风险通过间接估计社会困难并包括时间风险因素。实施健康计划中增强的哮喘风险预测模型可提供明显的优势对哮喘护理管理的投资并访问及时的索赔数据，可以在整个护理连续性中进行投资。因此，间隔研究的目的（社会标志物可以预测哮喘控制和急诊室访问）是1）描述私人保险的成年人和哮喘儿童的社会和财务困难，以及与药物依从性和恶化的关联，2）间接估计自我报告的社会以及使用常规收集健康计划和空间数据的财务困难，以及3）开发和验证机器学习网络模型，将时间社会标志物，临床和环境数据纳入到在健康计划中预测哮喘恶化。该研究利用了独特的研究人口医学部的环境，哈佛医学学术系学校，位于哈佛大学朝圣者卫生保健的区域非营利健康计划中。指导的职业发展奖将支持医师和健康服务研究人员Alon Peltz博士发展机器学习建模和使用社交数据的专业知识，以改善不良临床的预测结果。