Predicting and Preventing Pediatric Hospital Readmissions

 描述(由申请人提供)：防止再次住院，特别是可预防的或不必要的再次住院，已成为公共政策制定者、健康保险公司和医疗服务提供者感兴趣的领域。医院和州的儿科再住院率差别很大，有些情况，如早产，差别超过600%。识别具有最高再次住院风险的患者，使提供者能够制定干预措施，以减少或消除再次住院的机会。然而，大多数预测成人和儿童患者再入院风险的方法经常错误地对患者进行分类。这些算法中的大多数依赖于医院管理数据中识别的医疗因素，如住院原因和共存疾病的存在，作为风险计算模型的关键组件。这一特点忽略了(1)儿童在入院和出院时的情况；(2)门诊管理的特点，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、支助和照顾患病儿童的资源的措施，这可能是重新入院的额外风险因素。因此，针对早产儿的全原因再入院和特定模型的儿科模型具有较差的区分性，c-统计量介于0.6和0.7之间，从而导致较高的错误分类。这项研究的主要目标是开发儿科患者再入院风险的实时预测指标。这项提案将使用 开发和验证该工具的两种创新方法。首先，该项目将把儿科健康信息系统的住院数据与43家儿童医院的住院记录联系起来，这些医院照顾着全美22%的儿科住院，门诊保险数据要么来自Medicaid Analytic摘录文件，要么来自Health Care Institute，用于拥有私人保险或管理式医疗补助的儿童。这一广泛的患者队列将不仅提供关于医疗风险的信息，而且还将提供关于疾病严重性、共存健康状况的严重性以及获得高质量门诊护理的更好信息。其次，该项目将采用心理社会评估工具，这是一种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，从肿瘤学到普通儿科人口。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哪些儿童具有再次入院的最高风险，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.