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

描述(由申请者提供)：医院占美国医疗保健支出的近1万亿美元(约30%)。防止可避免的再次住院被认为是减少医疗保健浪费的一个关键机会。大约18%的患者在30天内再次入院。据估计，几乎14%的医疗保险重新入院--每年花费120亿美元--是可以预防的。尽管采取了以医院为中心的干预措施，但可以避免再入院 在过去的十年里稳步增长。门诊护理是减少再次入院的不可或缺的一部分。缺乏及时的初级保健医生(PCP)随访的患者再次入院的可能性是普通人的10倍。大约75%的出院摘要从未被初级保健医生收到，50%的重新入院的医疗保险患者从未进行过后续的办公室访问。一个综合的卫生信息技术系统(例如，卫生信息交换)可以增加初级保健方案在减少可避免的再入院方面的参与。HIE可以有效地协调医院和初级保健医生之间的护理，从而提高后续护理的及时性和可靠性。截至2012年，马里兰州的HIE(称为CRISP)收集了约400万名独立患者的实时数据，其中包括已由400多个PCP明确注册的25万名患者，以便使用CRISP的遭遇通知系统(ENS)进行特殊的交叉实践监测。ENS通知参与的提供者重新入院和其他哨兵事件。报告系统缺乏任何可操作的工具来“预测性地”确定患者再次入院的风险。这项研究的活动将受到马里兰医院和PCP社区为避免再次入院而处于重点和方案准备的高级阶段的显著催化。由于马里兰州的全额支付佣金，马里兰州的大多数医院并没有支付所有再入院的费用。所有的医院出院团队都经过了专门的培训，该州几乎所有的PCP现在都是先进的以患者为中心的护理疗养院(PCMH)模式的一部分。该项目将以约翰霍普金斯大学ACGs(调整后的临床小组，以前的门诊护理小组)为基础，该小组是全国在开发和测试预测模型方面最先进的小组之一。ACG已经在全国和其他15个国家使用了20年，并应用于6000多万患者，使用索赔和管理数据帮助预测各种医疗事件。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管理数据库，他们很少使用HIE数据来推导或验证。在这项探索性研究中，目标1是基于CRISP覆盖的人群开发和评估RRPM，以实时计算每个从马里兰医院出院的患者的风险分数。目标2将通过迭代增强过程测试RRPM的准确性，该过程将整合非HIE数据源，以探索最有价值的未来数据源(即将提供，但尚未提供给CRISP)；并在CCBC(新月市灯塔社区)测试CRISP的RRPM的普适性。最后，在目标3中，RRPM通知系统将成为原型，并集成到CRISP的ENS中。将进行一项定性研究，以评估HIE派生并交付的RRPM在参与的PCP中的潜在有效性和可用性。

DESCRIPTION (provided by applicant): 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.