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/概要 利用电子健康记录来描述和优化为儿童提供的护理 脑瘫是小儿最常见的一种先天性疾病，也是小儿最常见的一种先天性疾病。 就其严重程度、对治疗的反应、护理需求和对健康的影响而言， 为了孩子和家庭。优化整个生命的健康和健康，并使新的研究 为了有效地测试各种途径，至关重要的是要制定全面的临床护理， 生物心理社会数据模型开发一个全面的模型将加快和改善 了解，护理和进一步的研究，包括确定新的目标， 干预措施。该提案的首要目标是为CP相关表型、健康状况、护理活动和心理社会健康开发一个精确的健康模型，以实现个性化护理。为了实现这一目标，我们将自动收集、清理和整合从电子健康记录（EHR）中提取的基于多维、多领域和多队列的“大数据”，并将该EHR数据与前瞻性地结合联合收割机。 收集的高分辨率临床、功能、环境和心理社会数据。这项建议的重点将是6至12岁的儿童。初步工作表明，医疗中心提供照顾， 大约1,800名年龄在6至12岁之间的CP患者， 三年的EHR数据。我们将从这个EHR队列中招募200名儿童及其家人 家庭详细的表型。 招募将按照粗大运动功能分类系统（GMFCS）水平分层：60% GMFCS I，II或III（能够行走）和40% GMFCS IV或V（使用轮椅）。使用此 多队列设计将允许对影响护理接受、功能 成果、生活质量和参与。Aim 1将专注于创建一个多样化和全面的数据存储库 使用回顾性和前瞻性数据来表征实际与最佳护理（定义为 根据目前的循证文献）。目标2将导致发展接受护理 系数得分，并描述最佳护理程度如何与功能，质量 生活和参与，控制功能状态和年龄。[目标2中开发的模型将 转化为临床决策工具原型。Aim 3将展示以下概念的证明： 机器学习算法的可扩展性与PEDSnet学习健康系统。这个项目 创新性地将回顾性EHR数据与前瞻性临床数据相结合， 个人，治疗，家庭和环境因素与更多的接收 以证据为基础的护理和/或更好的结果。该项目将使该领域向精确化方向发展 为临床仪表板的开发奠定基础，以优化实践。]