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

通过对基于事件的护理进行自动化风险评估，实现基于价值的医疗保健

• 批准号: 9464424
• 负责人: Daniel Jay Riskin
• 金额: $ 22.26万
• 依托单位: CAPSICOHEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9464424
• 关键词: Attention; Cardiac; Caring; Chronic; Clinic; Clinical; Clinical Data; Complication; Continuity of Patient Care; Contracts; Cost Control; Data; Data Collection; Data Quality; Data Set; Diagnosis; Foundations; Goals; Health Care Reform; Healthcare; Healthcare Systems; Home environment; Hospitals; Hour; Insurance; Label; Measurement; Measures; Medicare; Modeling; Natural Language Processing; Ontology; Outpatients; Patient-Focused Outcomes; Patients; Phase; Phenotype; Procedures; Provider; Replacement Arthroplasty; Research; Resources; Risk; Risk Adjustment; Risk Assessment; Running; Small Business Innovation Research Grant; Technology; Testing; Text; Time; United States; Work; base; care episode; clinical care; clinically relevant; concept mapping; cost; feeding; financial incentive; improved; interest; oncology; payment; physical therapist; programs; social; success; vector

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.

项目概要 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，例如医疗保险 优点是，使用风险调整因子 (RAF) 下的年度风险衡量标准来提供财务激励 付款人和医院共同努力。 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事件， 肿瘤学诊断和心脏手术。在这些事件中，来自医院的多种类型的提供者 对于门诊物理治疗师来说，需要共同努力降低并发症的发生率 重新入院。风险水平用于调整付款人和提供者的付款并确定哪些 患者需要医院、诊所或家中的额外资源。 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功能，这需要 财务调整和准确、即时的信息，以调整给定的临床资源 案件。 3 4 更好的模型应包括与发作相关的所有病症，而不仅仅是慢性病 条件、添加社会决定因素以及检索信息的自动化方法 几个小时而不是几个月。因此，这个小企业创新研究 (SBIR) 第一阶段计划 包括以下具体目标： 1. 创建定义准确且全面的模型所需的表型分析组件 基于事件的风险，包括：（i）使用以下方法从临床数据中提取临床和社会特征 自然语言处理 (NLP)，(ii) 将包括社交特征在内的概念映射到本体 这将支持标准化数据使用，（iii）为每个记录构建一个特征向量，可以 用于构建考虑相关临床和社会风险的风险模型 2. 使用 10,000 例去识别的纵向临床数据验证表型成分 患者 在这个研究计划中，第一阶段将解决最困难的挑战，包括利用叙事 文本来识别影响护理事件的带有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尚未提供的关键基本特征 日期。第二阶段将建立在经过验证的技术的基础上，创建一个基于事件的风险模型，运行在 叙述性和离散的临床数据，并根据实际患者的结果进行测试。成功的标准将是 验证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。