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Predictive Analytics for Retention in HIV Care

HIV 护理保留的预测分析

基本信息

批准号：
10841315
负责人：
Jessica Ridgway
金额：
$ 7.35万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10841315
关键词：
AIDS prevention Advisory Committees Biometry Caring Case Management Case Manager Characteristics Chicago Client Clinical Data Clinical Informatics Continuity of Patient Care Data Data Analyses Data Analytics Data Science Data Sources Drug abuse EHR research Electronic Health Record Epidemiology Ethics Failure Foundations Future Goals HIV Health Healthcare Systems Hylobates Genus Individual Informatics Intervention Logistic Regressions Medical Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methodology Modeling Natural Language Processing Patient risk Patients Performance Persons Predictive Analytics Prevention Public Policy Research Research Activity Research Personnel Resources Risk Risk Factors Science Policy Social Sciences Structure Supervision Text Time Training United States career electronic data electronic health data follow-up high risk implementation science multidisciplinary patient engagement patient navigation predictive modeling predictive tools prevent programs random forest risk prediction skills social factors social media statistics symposium

项目摘要

Project Summary Retention in care is essential to HIV treatment and prevention, yet less than half of people living with HIV in the U.S. are retained in medical care. Effective retention interventions, such as intensive case management and patient navigation, are highly resource intensive. With diminishing resources for HIV care, better approaches are needed to identify patients at highest risk for retention failure who would most benefit from retention resources. A predictive model may quantify a specific patient’s risk of future retention-in-care failure based on his/her unique characteristics. Such a predictive model based on electronic health data and supplemental social factor informed data could be automated to generate risk prediction in real time. Instead of attempting to locate and re-engage patients who are “lost to follow-up” as is the current practice, a predictive model would allow case managers to identify at risk clients and intervene to prevent retention failure before it occurs. I have a strong background in clinical informatics, biostatistics, and epidemiology. Through this K23, I will further develop my skills in longitudinal data analysis and advanced data analytics and create a predictive model of retention in care. In Aim 1, I will create a predictive model of retention in care using EHR data from a large clinical data research network spanning 11 healthcare systems in Chicago, utilizing mixed effects logistic regression and random forest. Through Aim 2, I will evaluate whether the addition of supplemental social factor informed electronic data sources into the predictive model enhances its performance (e.g., unstructured text of EHR notes, geospatial data, social media data). Finally, in Aim 3, I will explore the feasibility of using the model in real time to increase retention efforts for at-risk patients. I will complete this project under the supervision of my mentor (Dr. John Schneider), co-mentor (Dr. David Meltzer), and my advisory team (Dr. Robert Gibbons, Rayid Ghani, and Dr. C. Hendricks Brown). Together, this multidisciplinary team brings nationally renowned expertise in HIV research, EHR research, longitudinal data analysis, natural language processing, social media data, implementation science, and ethics. In addition, they serve as Directors of the Chicago Center for HIV Elimination (Schneider), Center for Health and the Social Sciences (Meltzer), Center for Data Science and Public Policy (Ghani), Center for Health Statistics (Gibbons), and Center for Prevention Implementation Methodology for Drug Abuse and HIV (Brown). An integrated program of coursework, seminars, structured mentorship, research activities, and conferences will provide me with the skills necessary to complete the proposed research and transition to independence. My long-term career goal is to become an independent investigator utilizing HIV informatics to develop prediction models and tools to inform HIV prevention and treatment across the HIV care continuum. The mentorship and training that I will receive through this K23 award will provide me with the foundation necessary to pursue that goal and this proposal will form the basis for future R01 proposals.

项目摘要继续接受护理对艾滋病毒治疗和预防至关重要，但在2010年，美国保留在医疗护理中。有效的保留干预措施，如强化案例管理，患者导航是高度资源密集型的。随着艾滋病毒护理资源的减少，需要确定保留失败风险最高的患者，他们将从保留中获益最多资源预测模型可以基于以下因素来量化特定患者未来保留护理失败的风险：他/她独特的特征。这样的预测模型基于电子健康数据和补充的社会因素通知数据可以被自动化以真实的时间生成风险预测。而不是试图定位和重新参与“失访”的患者，如目前的做法，预测模型将允许个案管理员识别有风险的客户并进行干预，以在客户流失发生之前防止其流失。我在临床信息学、生物统计学和流行病学方面有很强的背景。通过K23，我将进一步发展我在纵向数据分析和高级数据分析方面的技能，保持护理的模式。在目标1中，我将使用EHR数据创建一个护理保留的预测模型，一个大型临床数据研究网络，跨越芝加哥的11个医疗保健系统，利用混合效应逻辑回归和随机森林。通过目标2，我将评估是否增加了补充社会因素通知的电子数据源进入预测模型增强了其性能（例如，非结构化文本电子健康记录、地理空间数据、社交媒体数据）。最后，在目标3中，我将探讨使用该模型的可行性在真实的时间，以增加保留的努力，为高危患者。我将在我的导师（约翰施耐德博士），共同导师（大卫博士）的监督下完成这个项目 Meltzer）和我的顾问团队（Robert Gibbons博士、Rayid Ghani和C. Hendricks Brown）。在一起，这个多学科团队带来了全国知名的艾滋病毒研究，EHR研究，纵向数据分析、自然语言处理、社交媒体数据、实施科学和道德。此外，本发明还提供了一种方法，他们担任芝加哥艾滋病毒消除中心（施耐德），健康和社会中心科学（梅尔泽），数据科学和公共政策中心（加尼），卫生统计中心（吉本斯），和药物滥用和艾滋病毒预防实施方法中心（布朗）。集成课程计划，研讨会，结构化的导师，研究活动，和会议将为我提供具有完成拟议研究和过渡到独立所需的技能。我的长期我的职业目标是成为一名独立的研究人员，利用艾滋病毒信息学开发预测模型和工具，为整个艾滋病毒护理过程中的艾滋病毒预防和治疗提供信息。指导和培训我将通过这个K23奖获得的奖励将为我提供实现这一目标所必需的基础，该提案将构成未来R 01提案的基础。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Measuring Retention in HIV Care in the First Year of the COVID-19 Pandemic: The Impact of Telehealth.

DOI：
10.1007/s10461-022-03875-3
发表时间：
2023-05
期刊：
AIDS and behavior
影响因子：
4.4
作者：
Ridgway JP;Massey R;Mason JA;Devlin S;Friedman EE
通讯作者：
Friedman EE

No-show Prediction Model Performance Among People With HIV: External Validation Study.

DOI：
10.2196/43277
发表时间：
2023-03-29
期刊：
JOURNAL OF MEDICAL INTERNET RESEARCH
影响因子：
7.4
作者：
Mason, Joseph A.;Friedman, Eleanor E.;Rojas, Juan C.;Ridgway, Jessica P.
通讯作者：
Ridgway, Jessica P.

Impact of mail order pharmacy use and travel time to pharmacy on viral suppression among people living with HIV.

DOI：
10.1080/09540121.2020.1757019
发表时间：
2020-11
期刊：
AIDS care
影响因子：
1.7
作者：
Ridgway JP;Friedman EE;Choe J;Nguyen CT;Schuble T;Pettit NN
通讯作者：
Pettit NN

Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.

DOI：
10.1093/jamiaopen/ooac033
发表时间：
2022-07
期刊：
JAMIA OPEN
影响因子：
2.1
作者：
Ridgway, Jessica P.;Mason, Joseph A.;Friedman, Eleanor E.;Devlin, Samantha;Zhou, Junlan;Meltzer, David;Schneider, John
通讯作者：
Schneider, John

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.

DOI：
10.2196/43017
发表时间：
2023-05-17
期刊：
JMIR PUBLIC HEALTH AND SURVEILLANCE
影响因子：
8.5
作者：
Mason, Joseph A.;Friedman, Eleanor E.;Devlin, Samantha A.;Schneider, John A.;Ridgway, Jessica P.
通讯作者：
Ridgway, Jessica P.