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（由申请人提供）：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、健康保险公司和服务提供者感兴趣的领域。医院和州的儿科再入院率差异很大，某些情况（例如早产）的再入院率差异超过 600%。识别再入院风险最高的患者可以让医疗服务提供者制定干预措施，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。这些算法大多数依赖于医院管理数据中确定的医疗因素，例如住院原因和共存医疗状况的存在，作为风险计算模型的关键组成部分。这一特征忽略了（1）孩子入院和出院时的状况； （二）门诊管理的特点，如医疗服务的可及性、门诊服务人员的质量等； (3) 更详细的家庭结构、支持和照顾患病儿童的资源措施，这可能是重新入院的额外风险因素。因此，全因再入院的儿科模型和早产儿的特定模型的区分度很差，c 统计量在 0.6 到 0.7 之间，因此错误分类率很高。本研究的主要目标是开发儿科患者再入院风险的实时预测器。本提案将使用 开发和验证该工具的两种创新方法。首先，该项目将把来自儿科健康信息系统的住院数据、来自 43 家儿童医院的住院记录（这些医院负责全美 22% 的儿科住院治疗）与来自医疗补助分析提取文件（针对拥有医疗补助保险的儿童）的门诊保险数据或来自医疗保健成本研究所（针对拥有私人保险或管理式医疗补助医疗补助的儿童）的门诊保险数据联系起来。这一广泛的患者群体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、共存健康状况的严重程度以及获得优质门诊护理的更好信息。其次，该项目将采用社会心理评估工具，这是一个评估家庭风险和资源的7个量表工具，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来降低这种风险。