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.

项目摘要 基于价值的医疗保健实施依赖于理解风险。 1个早期型号，例如Medicare 优势，使用风险调整因子（RAF）下的年度风险度量来提供经济激励 给付款人和医院一起工作。 2种更高级的模型，例如捆绑付款，目标 质量可变性最大的时期，特别是关节置换等护理时期， 肿瘤学诊断和心脏手术。在这些情节中，医院的许多类型的提供者 要门诊物理治疗师，需要共同努力，以降低并发症的速度和 再入院。风险水平用于调整付款人和提供商的付款，并确定哪个 患者需要在医院，诊所或家庭中额外的资源。 不幸的是，现有风险模型缺乏基于情节的护理所需的关键功能，这需要 财务一致性以及准确而直接的信息，以调整给定的临床资源 案件。 3 4更好的模型将包括与情节相关的所有条件，而不仅仅是慢性 条件，添加社会决定因素以及一种自动化方法来检索信息 小时而不是几个月。因此，这项小型企业创新研究（SBIR）I阶段计划 包括以下特定目标： 1。创建定义准确和全面模型所需的表型组件 基于情节的风险，包括：（i）从临床数据中提取临床和社会特征 自然语言处理（NLP），（ii）将包括社会特征在本体论的地图概念 这将支持标准化的数据使用，（iii）为每个记录构建一个功能向量，可以是 用于喂养相关临床和社会风险的风险模型 2。使用未识别的纵向临床数据验证表型成分的10,000 患者 在该研究计划中，第一阶段将应对最困难的挑战，包括利用叙事 文本以识别有时间标记的社会和临床特征，影响了一集的护理情节。成功标准 将准确地识别在风险模型中尚未可用的关键基础功能 日期。第二阶段将建立在经过验证的技术的基础上，以创建基于情节的风险模型。 叙事和离散的临床数据，并针对实际患者预后进行了测试。成功标准将是 验证了基于情节的风险模型，以支持基于价值的合同和基于价值的临床护理。