Leveraging advanced clinical phenotyping to enhance problem lists and support value-based healthcare

利用先进的临床表型来增强问题清单并支持基于价值的医疗保健

基本信息

批准号：
9762237
负责人：
Daniel Jay Riskin
金额：
$ 74.96万
依托单位：
VERANTOS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9762237
关键词：
Address Agreement American Area Artificial Intelligence Big Data Biotechnology Businesses Caring Characteristics Clinical Clinical Data Continuity of Patient Care Contracts Coughing Data Data Quality Data Set Databases Development Disease Feedback Foundations Garbage General Hospitals Gold Healthcare Healthcare Systems Heart failure Knowledge Letters Malignant Neoplasms Manuals Massachusetts Medical Modeling Natural Language Processing Noise Pathway interactions Patient Care Patients Peer Review Phase Physicians Primary Care Physician Provider Research Resources Review Literature Risk Semantics Signal Transduction Small Business Innovation Research Grant Smoking Source Symptoms System Systems Integration Technology Text Time United States United States Department of Veterans Affairs Update Writing base care delivery clinical decision support clinical phenotype comparative effectiveness cost discrete data effectiveness research evidence base expectation falls health data health management high reward high risk improved individual patient interest payment personalized care personalized medicine point of care population health programs risk sharing screening success support tools treatment planning

项目摘要

Project Summary With newly available electronic health data and a massive increase in processing power, data-driven personalized medicine is just now becoming possible.1 However, advances to improve health care are inherently limited by data quality. One of the most used sources of data, the patient problem list, is also the greatest source of data inaccuracy. According to recent studies, the patient problem list is often less than 50% accurate in documenting the most critical conditions.2 3 4 5 These errors exacerbate inefficiencies throughout the American health care system from care delivery to quality improvement. Primary care physicians rely on problem lists to develop transitional treatment plans for the 68 million Americans who change providers every year. Errors related to care transitions harm more than 1.5 million people each year in the United States, costing the nation an estimated $3.5 billion annually.6 Population health efforts, a cornerstone of value-based healthcare, rely on problem lists to determine risk levels and deployment of resources. These efforts cannot succeed if the source data produce faulty results. This application seeks to enable better individual patient care, enhanced population health management, and effective downstream analytics by building an automated problem list builder, which provides an accurate and granular account of the patient’s medical conditions. If the program is successful, one of the greatest technical risks in value-based healthcare will be addressed. Phase I exceeded success criteria in proving feasibility of core modules in natural language processing (NLP) and artificial intelligence. Based on Phase I success, implementation pathways are demonstrated through pilots with one of the largest US healthcare systems and one of the largest global biotechnology firms. The team is comprised of commercial and academic leaders in the field of NLP-based products applied to value-based healthcare.

项目摘要随着新可用的电子健康数据和处理能力的大量增加，数据驱动 1但是，改善医疗保健的进步是本质上受数据质量的限制。最常用的数据来源之一是患者问题列表，也是数据不准确的最大来源。根据最近的研究，患者问题清单通常很少在记录最关键条件方面的准确性50％。23 4 5这些错误加剧了整个美国医疗保健系统的效率低下从护理提供到质量改进。初级保健医师依靠问题清单来制定6800万每年改变提供商的美国人。与护理过渡有关的错误危害超过1.5 美国每年有百万人民，每年损失35亿美元。6 人口健康工作是基于价值医疗保健的基石，依靠问题清单来确定风险水平和资源部署。如果源数据产生错误，这些努力将无法成功结果。该应用程序旨在启用更好的个人患者护理，增强人口健康通过构建自动化问题列表构建器，管理和有效的下游分析，该构建器提供了对患者医疗状况的准确和细粒度的说明。如果程序是成功，将解决基于价值的医疗保健中最大的技术风险之一。第一阶段超出了在提供自然语言处理中核心模块可行性方面的成功标准（NLP）和人工智能。根据第一阶段的成功，实施途径被证明通过拥有美国最大的医疗保健系统之一和全球最大的全球生物技术之一的飞行员公司。该团队由基于NLP的产品领域的商业和学术领导者组成应用于基于价值的医疗保健。