A Community HIE-based Hospital Readmission Risk Prediction & Notification System

基于社区 HIE 的医院再入院风险预测

• 批准号: 8620097
• 负责人: Hadi Kharrazi
• 金额: $ 14.76万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2015-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8620097
• 关键词: Community; HIE; based; Hospital; Readmission

v Project Summary Hospitals account for nearly $1 trillion (~30%) of US health care spending. Preventing avoidable hospital readmissions is considered a key opportunity for reducing waste in health care. Around 18% of patients are readmitted to hospitals within 30 days. It is estimated that almost 14% of Medicare readmissions - costing $12 billion annually - are preventable. Despite hospital-centric interventions 'avoidable' readmissions have increased steadily over the last decade. Ambulatory care is integral to reducing hospital readmissions. Patients lacking timely Primary care physician (PCP) follow ups are 10 times more likely to be readmitted. Around 75% of discharge-summaries are never received by PCPs and 50% of readmitted Medicare patients never had a follow up office visit. An integrated health IT system (e.g., a health information exchange) can increase PCP engagement in reducing avoidable readmissions. HIEs can efficiently coordinate care between hospitals and PCPs thus improving the promptness and reliability of follow-up care. As of 2012, Maryland's HIE (known as 'CRISP') collects real-time data of ~4 million unique patients, including 250k patients that have been explicitly registered by more than 400 PCPs for special cross-practice monitoring using CRISP's Encounter Notification System (ENS). ENS notifies participating providers of readmission and other sentinel events. The reporting system lacks any actionable tool to 'predictively' identify the patient's risk of readmission. The activities of this research will be significantly catalyzed by Maryland hospital and PCP community's advanced stage of focus and programmatic readiness to avoid readmission. Most hospitals in Maryland are not paid for all readmissions due to state's all-payer commission. All hospital discharge teams are especially trained and virtually all PCPs in the state are now part of an advanced patient centered care medical home (PCMH) model. The project will be based within The Johns Hopkins ACGs (adjusted clinical groups, formerly ambulatory care groups) that is one of the most advanced in the nation in developing and testing predictive models. ACG has been used for two decades across the nation and in 15 other countries, and applied to 60+ million patients to help predict various healthcare events using claims and admin data. While readmission risk predictive models (RRPM) are very common post-facto using health plan claims and hospital administrative databases, they have rarely utilized HIE data for derivation or validation. In this exploratory research, Aim 1 is to develop and evaluate an RRPM based on the population covered by CRISP to calculate a risk score for each patient discharged from Maryland hospitals in real-time. Aim 2 will be to test the RRPM's accuracy through an iterative enhancing process that will integrate non-HIE data sources to explore the most valuable future data sources (soon to be, but not yet available to CRISP); and, to test the generalizability of CRISP's RRPM at CCBC (Crescent City Beacon Community). Finally, in Aim 3 the RRPM notification system will be prototyped and integrated into CRISP's ENS. A qualitative study will be conducted to evaluate the potential effectiveness and usability of the HIE-derived-and-delivered RRPM among participating PCPs.

v项目总结 医院占美国医疗保健支出的近1万亿美元（约30%）。预防可避免的再入院是 被认为是减少医疗保健浪费的关键机会。大约18%的患者在2015年内再次入院。 30天.据估计，近14%的医疗保险再入院-每年花费120亿美元-是可以预防的。尽管 以医院为中心的干预措施“可避免的”再入院在过去十年中稳步增加。 门诊护理是减少再入院的必要手段。缺乏及时初级保健医生（PCP）的患者 不稳定性高血压患者再次入院的可能性是正常人的10倍。大约75%的出院摘要从未被PCP收到，50%的出院摘要从未被PCP收到。 再次入院的医疗保险患者从未接受过随访。一个集成的医疗IT系统（例如，健康信息 交换）可以增加PCP参与减少可避免的再入院。 HIE可以有效地协调医院和PCP之间的护理，从而提高随访的准确性和可靠性 在乎截至2012年，马里兰州的HIE（被称为“CRISP”）收集了约400万独特患者的实时数据，其中包括25万名患者。 已由400多名PCP明确登记的患者，使用CRISP进行特殊的跨实践监测 遭遇通知系统（ENS）。ENS通知参与提供者再入院和其他前哨事件。的 报告系统缺乏任何可操作的工具来“预测性地”识别患者的再入院风险。 这项研究的活动将显着催化马里兰州医院和PCP社区的先进阶段， 重点和方案准备，以避免重新接纳。马里兰州的大多数医院不会为所有到期的再入院支付费用 州政府的所有付款人委员会所有的出院团队都经过专门培训，该州几乎所有的PCP现在都 先进的以患者为中心的医疗之家（PCMH）模式的一部分。 该项目将基于约翰霍普金斯ACG（调整后的临床组，以前的门诊护理组）， 是美国开发和测试预测模型最先进的公司之一。ACG已经使用了二十年 在全国和其他15个国家，并应用于60多万患者，以帮助预测各种医疗保健事件 使用索赔和管理数据。 虽然再入院风险预测模型（RRPM）是非常常见的事后使用健康计划索赔和医院 尽管这些数据库是行政管理数据库，但它们很少利用HIE数据进行推导或验证。在这项探索性研究中，目标1 是根据CRISP覆盖的人群开发和评估RRPM，以计算每位患者的风险评分 从马里兰州医院出院的病人。目标2将是通过迭代增强测试RRPM的准确性 整合非HIE数据源以探索最有价值的未来数据源的过程（即将实现，但尚未实现 CRISP可用）;以及，在CCBC（新月市灯塔社区）测试CRISP的RRPM的普遍性。最后， 在目标3中，RRPM通知系统将进行原型设计并集成到CRISP的ENS中。将进行定性研究 进行评估，以评价HIE衍生和交付的RRPM的潜在有效性和可用性， 参与PCP。