An Evaluation of Novel Domains for Predicting 30-Day Readmission

对预测 30 天再入院的新领域的评估

• 批准号: 8576427
• 负责人: Salomeh Keyhani
• 金额: $ 73.86万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8576427
• 关键词: Accounting; Acute myocardial infarction; Adopted; Affect; Alcohol abuse; Algorithms; Area; Caring; Characteristics; Clinical Data; Congestive Heart Failure; Country; Data; Data Set; Development; Disadvantaged; Disease; Distress; Electronic Health Record; Evaluation; Family; Goals; Health Status; Health system; Hospitals; Housing; Human; Incentives; Informatics; Intervention; Length of Stay; Life; Measures; Medicare; Methods; Modeling; Natural Language Processing; Patient Care; Patients; Performance; Pneumonia; Population; Predictive Factor; Publishing; Reporting; Resources; Risk; Risk Factors; Self Management; Severity of illness; Social support; Stroke; Substance abuse problem; Technology; Testing; Text; United States Centers for Medicare and Medicaid Services; Variant; Veterans; cost; demographics; design; fall risk; falls; functional status; health administration; high risk; high risk behavior; hospital patient care; hospital readmission; improved; marginally housed; mortality; novel; payment; programs; public health relevance; social; tool

DESCRIPTION (provided by applicant): The Centers for Medicare and Medicaid Services has proposed to financially penalize hospitals that have 30-day readmission rates above the national mean. As a result hospitals caring for disadvantaged populations with more needs might be penalized by current 30-day readmission models that do not include measures of social risk and functional status of the patients served. These are two important variable domains that directly impact a patient's ability to manage their disease. Social risk factors (e.g. living alone, social support, marginal housing, and alcohol abuse) and functional status (e.g. mobility, fall risk) are rarely present in administrative data, which is why so few readmission models include this data. Yet many of these variables are available in electronic health records (EHR) and the advancement of the field of informatics has made the extraction of these data feasible. These variables may improve the discriminative ability of 30-day readmission models which currently explain little of the variation in readmission rates among patients. We propose to improve 30-day readmission models by extracting measures of social risk and functional status from the EHR using the novel method of Natural Language Processing (NLP). We will combine administrative data (VA and Medicare) and data extracted from the national EHR in the VA for 6000 patients 65 and older in 2011 to improve upon currently available 30-day hospital readmission risk prediction models for congestive heart failure (CHF), acute myocardial infarction (AMI), pneumonia and stroke. We have chosen these conditions because hospital-level 30-day readmission rates for these conditions (CHF, AMI and pneumonia) are currently or will soon be (stroke) publicly reported. Our proposal has two goals: 1) to develop, test and evaluate automated NLP algorithms designed to extract measures of social risk and functional status from the EHR and 2) to understand the impact of these two novel domains on 30-day readmission across four conditions with fundamentally different post-discharge hospital course and disease trajectories. We propose a paradigm shift in the understanding and obtainment of factors predictive of 30-day readmission. Our overarching hypothesis is that social risk factors and functional status which directly influence a patient's self-management ability are critical factors predictive of 30-day readmission, can be extracted from the EHR, and should be included in risk prediction models. The development of better risk prediction models will allow the identification of patients at highest risk of readmission and facilitate post-discharge interventions in their care. In addition, if social risk factors and functional status are criticalin explaining variation in 30-day readmission rates, then hospitals that care for patients with a higher burden of social risk and functional needs may be inappropriately penalized by current risk predictions models that lack these measures. Also, as more hospitals adopt EHRs, we need to study more advanced technologies such as automated NLP as tools to efficiently extract information and to inform health systems about the characteristics of the patients they serve.

描述（由申请人提供）：医疗保险和医疗补助服务中心已提议在财务上罚款30天的再入院率高于国家平均值的医院。结果，当前30天的再入院模型不包括社会风险措施和服务患者的功能状况，这可能会受到关注有更多需求的处境不利人群的医院。这是两个重要的变量领域，它们直接影响患者管理疾病的能力。社会风险因素（例如，单独生活，社会支持，边际住房和酗酒）和功能状况（例如流动性，跌落风险）很少存在于行政数据中，这就是为什么很少有这样的再入再入再入院模型包含此数据的原因。然而，这些变量中有许多可在电子健康记录（EHR）中获得，并且信息学领域的发展使得这些数据可行。这些变量可能会提高30天再入院模型的判别能力，这些模型目前很少解释患者的再入院率变化。 我们建议通过使用新颖的自然语言处理方法（NLP）从EHR中提取社会风险和功能状况的度量来改善30天的再入院模型。我们将在VA中为6000名患者在2011年以上的65名患者中从国家EHR中提取的行政数据（VA和Medicare）和数据结合，以改善目前可用的30天医院再入院风险预测通信性心力衰竭（CHF），急性心肌梗死（AMI），肺炎和Stroke。我们之所以选择这些条件，是因为目前或即将公开报告（中风）的医院级别30天的再入院率（CHF，AMI和肺炎）。我们的建议有两个目标：1）开发，测试和评估旨在从EHR提取社会风险和功能状况的自动化NLP算法和2））了解这两个新型领域对四个条件的30天重新入院的影响，其基本上不同的后医院治疗和疾病轨迹基本上不同。 我们提出了对30天再入院的因素的理解和获得的范式转变。我们的总体假设是，直接影响患者自我管理能力的社会风险因素和功能状况是预测30天再入院的关键因素，可以从EHR中提取，应包括在风险预测模型中。更好的风险预测模型的发展将允许鉴定有最高再入院风险的患者，并促进了护理后的分期干预措施。此外，如果社会风险因素和功能状况是重要的解释30天再入院率的差异，那么护理社会风险和功能需求负担较高的患者的医院可能会因缺乏这些措施的当前风险预测模型而受到不适当的惩罚。此外，随着越来越多的医院采用EHR，我们需要研究更先进的技术，例如自动化NLP作为工具，以有效提取信息，并向卫生系统提供有关其服务患者特征的信息。