Predicting and Preventing Pediatric Hospital Readmissions

 预防再入院，特别是可预防或不必要的再入院，已成为公共政策制定者，健康保险公司和提供者感兴趣的领域。医院和州的儿科再入院率差异很大，有些情况下，如早产，差异超过600%。识别出再入院风险最高的患者，使医疗服务提供者能够制定干预措施，以减少或消除再入院的机会。然而，大多数预测成人和儿童患者再入院风险的方法经常对患者进行错误分类。这些算法中的大多数依赖于在医院管理数据中识别的医疗因素，例如住院的原因和共存的医疗状况的存在，作为风险计算模型的关键组成部分。这一特征忽略了（1）儿童在入院和出院时的状况;（2）门诊管理的特征，如获得医疗保健的机会和门诊提供者的质量;（3）更详细的家庭结构、支持和资源的测量，以照顾生病的孩子，这可能是再入院的额外风险因素。因此，全因再入院和早产儿特定模型的儿科模型的区分度都很差，c统计量在0.6和0.7之间，因此分类错误率很高。本研究的主要目标是开发一种儿科患者再入院风险的实时预测器。该提案将使用 两个创新的方法来开发和验证这个工具。首先，该项目将把来自儿科健康信息系统的住院患者住院数据与来自43家儿童医院的住院患者住院记录联系起来，这些医院负责全美22%的儿科住院患者，使用来自Medicaid Analytic Extract文件（适用于有Medicaid保险的儿童）或来自The Health Care Cost Institute（适用于有私人保险或托管保险的儿童）的门诊保险数据，医疗保健这一广泛的患者队列不仅将提供有关医疗风险的信息，还将提供有关疾病严重程度、共存健康状况严重程度和获得优质门诊护理的更好信息。其次，该项目将调整心理社会评估工具，这是一个7级工具，用于评估家庭风险和资源，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，从肿瘤到一般儿科人群。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，使提供者能够实时识别那些再入院风险最高的儿童，并有针对性地采取干预措施以降低这种风险。

 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.