An Evaluation of Novel Domains for Predicting 30-Day Readmission

描述(由申请人提供)：医疗保险和医疗补助服务中心建议对30天再住院率高于全国平均水平的医院进行经济处罚。因此，照顾有更多需求的弱势人群的医院可能会受到目前30天再入院模式的惩罚，这种模式不包括对所服务患者的社会风险和功能状况的衡量。这两个重要的可变域直接影响患者管理疾病的能力。社会风险因素(例如独居、社会支持、边缘住房和酗酒)和功能状态(例如流动性、跌倒风险)很少出现在管理数据中，这就是为什么很少有重新接纳模型包括这些数据。然而，这些变量中的许多都可以在电子健康记录(EHR)中获得，信息学领域的进步使这些数据的提取成为可能。这些变量可能会提高30天再入院模型的区分能力，该模型目前很少解释患者再入院率的差异。我们建议通过使用自然语言处理(NLP)的新方法从EHR中提取社会风险和功能状态的度量来改进30天再入院模型。我们将结合行政数据(退伍军人管理局和联邦医疗保险)和从退伍军人事务部国家电子病历中提取的数据，在2011年为6000名65岁及以上的患者提供数据，以改进目前可用的充血性心力衰竭(CHF)、急性心肌梗死(AMI)、肺炎和中风的30天再入院风险预测模型。我们之所以选择这些情况，是因为这些情况(心力衰竭、急性心肌梗死和肺炎)的医院级30天再住院率目前或即将(中风)公开报道。我们的建议有两个目标：1)开发、测试和评估自动NLP算法，旨在从EHR中提取社会风险和功能状态的度量；2)了解这两个新领域对出院后病程和疾病轨迹完全不同的四种情况下30天再次住院的影响。我们建议在理解和获得预测30天再入院的因素方面进行范式转变。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态是预测30天再入院的关键因素，可以从EHR中提取，并应包括在风险预测模型中。开发更好的风险预测模型将允许识别具有最高再入院风险的患者，并促进出院后护理干预。此外，如果社会风险因素和功能状况是解释30天再住院率差异的关键因素，那么照顾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电子病历，我们需要研究更先进的技术，如自动NLP，作为有效提取信息和向卫生系统告知他们所服务的患者的特征的工具。

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.