The Intersection of Personalized Medicine and Implementation Science to Improve Healthcare Utilization in Cirrhosis

个性化医疗与实施科学的交叉点提高肝硬化医疗保健利用率

• 批准号: 10055401
• 负责人: Archita P. Desai
• 金额: $ 18.17万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10055401
• 关键词: Admission activity; Algorithms; Biometry; Brain; Car Phone; Caregiver support; Caring; Chronic Disease; Cirrhosis; Complement; Data; Data Analyses; Data Sources; Databases; Dementia; Disease Management; Emergency department visit; Face; Future; Goals; Guidelines; Health; Health Care Reform; Health Technology; Health system; Healthcare; Healthcare Systems; Hospitalization; Individual; Intervention; Liver; Liver diseases; Measures; Medical Records; Mentors; Mentorship; Methodology; Mobile Health Application; Modeling; Outcome Measure; Patient Education; Patient Outcomes Assessments; Patient Rights; Patients; Performance; Population; Predictive Factor; Prospective Studies; Prospective cohort; Research; Resources; Risk; Symptoms; Testing; Time; Training; Translating; Vulnerable Populations; base; big data management; cohort; cost; design; evidence base; experience; health application; health care service utilization; health data; high risk; hospital readmission; implementation science; improved; improved outcome; innovation; inpatient service; multidimensional data; novel; patient engagement; patient oriented; patient population; performance tests; personalized medicine; population health; predictive modeling; programs; prospective; readmission rates; skills; socioeconomics; success; successful intervention; tool

ABSTRACT Management of cirrhosis is resource-intensive and disproportionately contributes a growing burden on healthcare. Inpatient care is a sizeable portion of this burden where nearly 30% of admissions result in a readmission within 30 days. Unfortunately, health system-based interventions successful in reducing readmission rates face important barriers to dissemination. In order for successful health delivery redesign to occur, it is important to target the right patient and deliver a tailored intervention. Precisely segmenting patient populations to identify high utilizers is an important first step. Current readmission prediction models based on traditional medical records data have weak performance in cirrhosis. Instead, our novel preliminary data correlate patient reported outcome measures (PROMs) to future healthcare utilization (HCU). Further, the PI’s mentor has shown that when healthcare systems combine real-time PRO tracking with evidence-based management algorithms and patient-facing health tools, HCU burden can be reduced. Based on these data, this proposal will first test the overarching hypothesis that a combination of EHR-based and non-EHR, patient-centered measures will better identify high utilizers in cirrhosis. Taken a step further, this proposal will also test the hypothesis that successful, scalable models of care can be translated to high-risk cirrhotics through adaptation of a health technology tool. We will test these hypotheses via three aims and a robust training plan. Specific Aim #1 will assess HCU prediction by existing risk models and then utilize a state-wide data source to further refine risk prediction with liver disease-specific and population health data. Specific Aim #2 will further calibrate prediction of future HCU using PROMs in a prospective cohort of hospitalized cirrhotics. With the ability to identify a vulnerable group of cirrhotics from SA#s 1-2, Specific Aim #3 will build on the co-mentor’s (Dr. Boustani) success in improving HCU in dementia populations by adapting Brain Care Notes, a mobile phone health application designed to support real-time symptom tracking, care-giver support and engagement to reduce HCU in those with cirrhosis. Further, while completing these aims, the PI will accomplish 3 interdisciplinary training goals: 1) develop advanced biostatistical and big data management and analysis skills; 2) acquire experience in methodologies needed for the study of PROMs; 3) gain expertise in healthcare implementation science research all under the guidance of a robust mentorship team led by national experts in the proposed fields. Successful completion of these aims will support the design of a future R01-level intervention that provides innovative, scalable solutions for the chronic disease management in cirrhosis.

摘要 肝硬化的管理是资源密集型的，并且不成比例地增加了患者的负担。 健康护理住院护理是这一负担的相当大的一部分，其中近30%的入院导致 30天内再入院。不幸的是，基于卫生系统的干预措施成功地减少了 重新接纳率在传播方面面临重大障碍。为了成功地重新设计保健服务， 当发生这种情况时，重要的是要瞄准正确的患者并提供量身定制的干预。精确分割患者 人口，以确定高利用率是一个重要的第一步。当前再入院预测模型基于 传统的病历数据在肝硬化方面表现不佳。相反，我们新的初步数据与 患者报告的结局指标（PROM）与未来医疗保健利用（HCU）。此外，PI的导师 表明当医疗保健系统将联合收割机实时PRO跟踪与循证管理相结合时 算法和面向患者的健康工具，可以减少HCU的负担。根据这些数据，该提案将 首先检验总体假设，即结合基于EHR和非EHR，以患者为中心的措施 将更好地识别肝硬化中的高利用率。更进一步说，这一提议还将检验以下假设： 成功的、可扩展的护理模式可以通过适应健康状况， 技术工具。我们将通过三个目标和一个强大的培训计划来测试这些假设。具体目标#1 通过现有风险模型评估HCU预测，然后利用全州范围的数据源进一步细化风险 预测肝病特异性和人群健康数据。具体目标#2将进一步校准预测 在住院患者的前瞻性队列中使用PROM的未来HCU。有能力识别 来自SA#s 1-2、具体目标#3的弱势群体，将在共同导师（Boustani博士）的基础上建立 通过调整大脑护理笔记（一种移动的电话健康）， 旨在支持实时症状跟踪、护理人员支持和参与以减少HCU的应用程序 肝硬化的患者。此外，在完成这些目标的同时，PI将完成3个跨学科培训 目标：1）培养先进的生物统计和大数据管理和分析技能; 2）获得经验 研究PROM所需的方法; 3）获得医疗保健实施科学的专业知识 所有这些研究都在由拟议领域的国家专家领导的强大导师团队的指导下进行。 成功完成这些目标将支持未来R 01级干预措施的设计， 为肝硬化慢性病管理提供创新、可扩展的解决方案。