Enabling value-based healthcare through automating risk assessment for episode-based care

项目摘要 基于价值的医疗保健实施依赖于对风险的理解。1早期模式，如医疗保险 优势，使用风险调整因子（RAF）下的年度风险度量来提供财务激励 支付方和医院合作2更先进的模式，如捆绑支付，目标 质量变化最大的时期，特别是关节置换等护理阶段， 肿瘤诊断和心脏手术。在这些事件中，许多类型的提供者，从医院 门诊物理治疗师，需要共同努力，以减少并发症的发生率， 再入院风险水平用于调整付款人和提供者的付款，并确定 患者在医院、诊所或家中需要额外的资源。 不幸的是，现有的风险模型缺乏基于事件的护理所需的关键特征，这需要 财务调整和准确和即时的信息，以调整给定的临床资源 案子3 4一个更好的模型应该包括与一次发作有关的所有条件，而不仅仅是慢性病。 条件，社会决定因素的增加，以及自动检索信息的方法， 几个小时而不是几个月小企业创新研究（SBIR）第一阶段计划 包括以下具体目标： 1.创建定义准确和全面模型所需的表型分析组件 基于事件的风险，包括：（i）使用 自然语言处理（NLP），（ii）将包括社会特征的概念映射到本体 其将支持标准化数据使用，（iii）为可以 用于提供一个风险模型，该模型考虑了相关的临床和社会风险 2.使用10，000例患者的去识别纵向临床数据对表型组分进行分类 患者 在这项研究计划中，第一阶段将解决最困难的挑战，包括利用叙事 文本，以识别影响护理事件的时间标记的社会和临床特征。成功标准 将准确识别风险模型中没有的关键基本特征， 约会第二阶段将在经过验证的技术基础上建立一个基于事件的风险模型， 叙述性和离散的临床数据，并根据实际患者结局进行测试。成功的标准将是 经验证的基于事件的风险模型，以支持基于价值的合同和基于价值的临床护理。

Project Summary Value-based healthcare implementation relies on understanding risk. 1 Early models, such as Medicare Advantage, use annual measures of risk under a risk adjustment factor (RAF) to offer financial incentive to payers and hospitals to work together. 2 More advanced models, such as bundled payments, target the periods of greatest quality variability, specifically episodes of care such as joint replacement, oncology diagnosis, and cardiac procedures. In these episodes, many types of providers, from hospitals to outpatient physical therapists, need to work together to reduce rates of complication and readmission. Risk levels are used to adjust payment for payer and providers and to determine which patients require additional resources in the hospital, clinic, or home. Unfortunately, existing risk models lack key features needed for episode-based care, which requires both financial alignment and accurate and immediate information to adjust clinical resources for a given case. 3 4 A better model would include all conditions relevant to an episode rather than just chronic conditions, addition of social determinants, and an automated approach to retrieve the information in hours rather than months. Thus, this Small Business Innovation Research (SBIR) Phase I program includes the following Specific Aims: 1. Create the phenotyping components required to define an accurate and comprehensive model of episode-based risk, including: (i) extract clinical and social features from clinical data using natural language processing (NLP), (ii) map concepts including social features to an ontology that will support normalized data use, (iii) build a feature vector for each record that can be used to feed a risk model that accounts for relevant clinical and social risk 2. Validate the phenotyping components using de-identified longitudinal clinical data for 10,000 patients In this research program, Phase I will tackle the most difficult challenges, including leveraging narrative text to recognize time-labeled social and clinical features influencing an episode of care. Success criteria will be accurate recognition of key underlying features that have not been available in risk models to date. Phase II will build upon the validated technology to create an episode-based risk model run on narrative and discrete clinical data and tested against actual patient outcomes. Success criteria will be a validated episode-based risk model to support value-based contracting and value-based clinical care.