Leveraging the electronic health record to characterize and optimize care delivery for children with cerebral palsy.

利用电子健康记录来描述和优化脑瘫儿童的护理服务。

• 批准号: 10295949
• 负责人: Bruce J Aronow
• 金额: $ 70.02万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10295949
• 关键词: Address; Affect; Age; Algorithms; Big Data; Caring; Cerebral Palsy; Characteristics; Child; Childhood; Classification; Clinical; Clinical Data; Clinical Practice Guideline; Collection; Complex; Data; Descriptor; Development; Diagnosis; Dimensions; Electronic Health Record; Environmental Risk Factor; Evidence based practice; Exhibits; Family; Foundations; Future; Health; Health Status; Health system; Imaging Techniques; Individual; Institution; Intervention; Learning; Life; Literature; Machine Learning; Medical center; Methods; Mission; Modeling; Motor; Motor Skills; Natural Language Processing; Outcome; Patients; Patterns of Care; Personal Satisfaction; Pharmaceutical Preparations; Phenotype; Population Distributions; Precision Health; Prospective cohort; Protocols documentation; Quality of life; Recommendation; Research; Resolution; Severities; Socioeconomic Factors; Standardization; Subgroup; System; Testing; Time; Translating; United States National Institutes of Health; Walking; Wheelchairs; Work; base; biopsychosocial; care delivery; clinical care; clinical development; clinical phenotype; clinical practice; cohort; dashboard; data modeling; data repository; database query; design; electronic structure; evidence base; functional outcomes; functional status; improved; individual response; innovation; machine learning algorithm; machine learning method; motor impairment; novel; personalized care; physically handicapped; precision medicine; prospective; prototype; psychosocial; recruit; response; tool

Abstract/summary Leveraging the electronic health record to characterize and optimize care delivery for children with cerebral palsy: Cerebral palsy (CP) is the most common physical disability of childhood, but it is highly heterogeneous with respect to its severity, response to therapy, care needs, and impact on wellness for the child and family. To optimize health and wellness throughout life and enable new research avenues to be effectively tested, it is critical to develop a comprehensive clinical care and biopsychosocial data model. Development of a comprehensive model would both accelerate and improve understanding, care, and further research, including the identification of novel targets for interventions. The overriding objective of this proposal is to develop a precision health model for CP-related phenotypes, health status, care activities, and psychosocial well-being that will individualize care. To accomplish this objective, we will automate the collection, cleaning, and integration of multi-dimensional, multi-domain and multi-cohort based "big data" extracted from the electronic health record (EHR), and combine this EHR data with prospectively collected, high-resolution clinical, functional, environmental and psychosocial data. The focus of this proposal will be on children between ages 6 and 12 years. Preliminary work indicates that the medical center provides care for approximately 1,800 patients with CP who are between the ages of 6 and 12 years and have at least three years of EHR data. From this EHR cohort, we will prospectively recruit 200 children and their families for detailed phenotyping. Recruitment will be stratified by Gross Motor Functional Classification System (GMFCS) Levels: 60% GMFCS I, II, or III (able to walk) and 40% GMFCS IV or V (use a wheelchair). Using this multi-cohort design will allow for robust characterization of multi-dimensional factors that impact care receipt, functional outcomes, quality of life and participation. Aim 1 will focus on creating a diverse and comprehensive data repository using both retrospective and prospective data to characterize actual versus optimal care (defined by current evidence-based literature). Aim 2 will lead to development of a receipt of care coefficient score and characterize how the degree of optimal care relates to function, quality of life and participation, controlling for functional status and age. [Models developed in Aim 2 will be translated into a clinical decision tool prototype. Aim 3 will demonstrate proof of concept for scalability of machine learning algorithms with the PEDSnet Learning Health System. This project innovatively combines retrospective EHR data with prospective clinical data to elucidate individual, treatment, family, and environmental factors associated with greater receipt of evidence-based care and/or better outcomes. This project will move the field toward precision medicine for CP and create a foundation for development of clinical dashboards to optimize practice.]

摘要/总结 利用电子健康记录来描述和优化儿童护理服务 脑瘫：脑瘫 (CP) 是儿童期最常见的身体残疾，但其发病率很高。 在其严重程度、对治疗的反应、护理需求以及对健康的影响方面存在差异 为了孩子和家庭。优化一生的健康状况并促进新研究 要有效测试途径，制定全面的临床护理和 生物心理社会数据模型。综合模型的开发将加速并改进 理解、关心和进一步研究，包括确定新的目标 干预措施。该提案的首要目标是针对脑瘫相关表型、健康状况、护理活动和社会心理健康开发一个精确的健康模型，从而实现个性化护理。为了实现这一目标，我们将自动收集、清洗和整合从电子健康记录（EHR）中提取的多维度、多领域、多队列的“大数据”，并将这些EHR数据与前瞻性的数据相结合。 收集的高分辨率临床、功能、环境和社会心理数据。该提案的重点是 6 岁至 12 岁的儿童。初步工作表明，医疗中心为患者提供护理 大约 1,800 名 CP 患者年龄在 6 至 12 岁之间，并且至少患有 三年的 EHR 数据。从这个 EHR 队列中，我们将前瞻性地招募 200 名儿童及其 家庭进行详细的表型分析。 招聘将按粗大运动功能分类系统 (GMFCS) 级别进行分层：60% GMFCS I、II 或 III（能够行走）和 40% GMFCS IV 或 V（使用轮椅）。使用这个 多队列设计将允许对影响护理接受、功能的多维因素进行稳健的表征。 结果、生活质量和参与。目标 1 将专注于创建多样化且全面的数据存储库 使用回顾性和前瞻性数据来描述实际护理与最佳护理（定义 根据当前的循证文献）。目标 2 将导致护理收据的发展 系数得分并描述最佳护理程度与功能、质量的关系 生活和参与，控制功能状态和年龄。 [Aim 2 中开发的模型将 被转化为临床决策工具原型。目标 3 将展示概念验证 PEDSnet 学习健康系统的机器学习算法的可扩展性。这个项目 创新地将回顾性 EHR 数据与前瞻性临床数据相结合，以阐明 与更多接受治疗相关的个人、治疗、家庭和环境因素 基于证据的护理和/或更好的结果。该项目将推动该领域走向精准化 CP 医学并为开发临床仪表板以优化实践奠定基础。]