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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.

项目摘要随着新的电子健康数据和处理能力的大幅提高，个性化医疗现在才刚刚成为可能。1然而，改善医疗保健的进步受数据质量的限制。最常用的数据来源之一，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，病人的问题清单往往少在记录最关键的情况时，准确率超过50%。2 3 4 5这些错误加剧了从医疗服务到质量改进，整个美国医疗保健系统的效率低下。初级保健医生依靠问题清单为6800万人制定过渡性治疗计划美国人每年都会更换供应商。与护理过渡相关的错误伤害超过1.5 美国每年有200万人死亡，估计每年给国家造成35亿美元的损失。人口健康工作是基于价值的医疗保健的基石，它依赖于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，结果该应用旨在实现更好的个体患者护理，增强人口健康管理和有效的下游分析，通过构建自动化问题列表生成器，提供患者医疗状况的准确和详细说明。如果程序如果成功，将解决基于价值的医疗保健中最大的技术风险之一。第一阶段在证明自然语言处理核心模块的可行性方面超过了成功标准 (NLP)和人工智能在第一阶段成功的基础上，展示了实施途径通过与美国最大的医疗保健系统之一和全球最大的生物技术之一的试点，事务所该团队由基于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。