Predicting and Preventing Pediatric Hospital Readmissions

预测和预防儿科再入院

• 批准号: 9269175
• 负责人: Scott A Lorch
• 金额: $ 25万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9269175
• 关键词: Predicting; Preventing; Pediatric; Hospital; Readmissions

 DESCRIPTION (provided by applicant): The prevention of hospital readmissions, particularly preventable or unnecessary readmissions, has become an area of interest for public policy makers, health insurers, and providers. Hospital and state pediatric readmission rates vary widely, with some conditions, such as premature birth, varying by over 600%. Identifying patients at highest risk of readmission allows providers to develop interventions to reduce or eliminate the chance of a hospital readmission. However, most methods to predict the risk of readmission for both adult and pediatric patients frequently misclassify patients. Most of these algorithms rely on medical factors identified in hospital administrative data, such as the reason for hospitalization and the presence of co- existing medical conditions, as a key component of a risk calculation model. This feature ignores (1) the condition of the child at both the time of admission and the time of discharge; (2) features of the outpatient management, such as access to health care and the quality of the outpatient provider; and (3) more detailed measures of familial structure, support, and resources to care for a sick child that may be additional risk factors for readmission. As a result, pediatric models for both all-cause readmissions and specific models in the prematurely-born infant have poor discrimination, with c-statistics between 0.6 and 0.7, and consequent high misclassification. The principal goal of this study is to develop a real-time predictor of readmission risk for pediatric patients. This proposal will use two innovative approaches to develop and validate this tool. First, this project will link inpatien hospitalization data from the Pediatric Health Information System, with inpatient hospital records from 43 Children's Hospitals that care for 22% of pediatric hospitalizations from across the United States, with outpatient insurance data either from the Medicaid Analytic Extract files for children with Medicaid insurance or from The Health Care Cost Institute for children with private insurance or managed-care Medicaid. This broad cohort of patients will provide information not only on medical risk, but improved information on illness severity, severity of co-existing health conditions, and access to quality outpatient care. Second, this project will adapt the Psychosocial Assessment Tool, a 7-scale tool to assess family risks and resources, including family structure, emotional and behavioral concerns, marital/family problems, beliefs, and other stressors, from oncology to the general pediatric population. With the help of a patient/provider advisory committee, we will then develop readmission risk prediction models to allow providers in real-time to identify those children at highest risk for hospital readmission, and to target interventions to reduce this risk.

 描述（由适用提供）：预防医院再入院，尤其是可预防或不必要的再入院，已成为公共政策制定者，保险和提供者的关注领域。医院和州儿科再入院率在某些情况下（例如早产）差异很大，差异超过600％。确定重新入院风险的患者可以使提供者制定干预措施，以减少或消除医院再入院的机会。但是，大多数预测成人和小儿患者再次入院风险的方法经常错误分类。这些算法中的大多数都依赖于医院行政数据中确定的医疗因素，例如住院和共存医疗状况的存在，是风险计算模型的关键组成部分。此功能忽略了（1）在入院时间和出院时间的儿童状况； （2）门诊管理的特征，例如获得医疗保健和门诊提供者的质量； （3）更详细的家庭结构，支持和资源来照顾生病的孩子，这可能是再入院的其他风险因素。结果，过早出生的婴儿的全因再入院和特定模型的小儿模型的歧视差，C统计量在0.6至0.7之间，因此很高的错误分类。这项研究的主要目标是为小儿患者开发重新启动风险的实时预测指标。该建议将使用 开发和验证该工具的两种创新方法。首先，该项目将将儿科健康信息系统的Inpatien住院数据与来自43家儿童医院的住院医院记录联系起来，这些记录护理了美国各地的22％的儿科住院治疗，以及来自医疗补助的医疗补助档案中医疗保险保险的医疗保险档案中的医疗保险档案中的医疗保健费用医疗保健成本研究所的医疗保险档案，或者是由医疗保健成本研究所提供的。这种广泛的患者队伍不仅将提供有关医疗风险的信息，而且还提供了有关疾病严重程度，共存健康状况严重程度以及获得优质门诊护理的信息。其次，该项目将适应社会心理评估工具，这是一种评估家庭风险和资源的7级工具，包括家庭结构，情感和行为问题，婚姻/家庭问题，信仰和其他压力源，从肿瘤学到一般的儿科人群。在患者/提供者咨询委员会的帮助下，我们将开发再入院风险预测模型，以允许提供者实时确定那些患有医院再入院风险最高的孩子，并针对降低这种风险的干预措施。