SBIR Phase I: Development of Cohort Identification Tool

这个小型企业创新研究(SBIR)第一阶段项目旨在解决医疗保健中最重要和最具挑战性的软件需求：队列识别。队列是一组有共同疾病的患者。队列支持现代医疗，定义治疗算法，衡量质量改进，支持政府倡议，并代表研究试验的核心组织。虽然已经开发了手动技术来识别医疗保健组织的电子病历(EMR)中的队列，但所有技术都依赖于医生或编码员为每种适用的医疗条件识别和标记每一份记录。这种手动过程是不准确的，并且只解决了最常见的情况。建议的新颖和革命性方法是使用大数据技术，利用每个患者在每个医疗机构的每一次会面中记录的详细的非结构化叙事笔记。提取非结构化数据并使其在医疗保健中可用所需的核心技术是自然语言处理(NLP)与临床概念(本体)的编码表示相结合。该提案汇集了业界领先的团队和技术，以解决医疗保健领域最大的数据问题，这提供了一个独特的机会，可以显著影响未来几十年的医疗保健。此项目的更广泛影响/商业潜力包括为下一代数据驱动的医疗保健创建基础架构。正如谷歌和雅虎需要先进的信息提取和搜索索引技术来使海量互联网数据可用一样，医疗保健也需要类似的使能技术。考虑到医生使用的大量自然语言描述以及定义潜在队列和算法的复杂逻辑，医疗保健挑战甚至更加复杂。为了解决这些问题，医疗保健需要谷歌和雅虎使用的技术类别，但专门用于医疗保健领域。在医疗保健方面，质量改进需要认识到人群中的高危人群。错过这些队列并对他们进行适当的治疗可能会使死亡率增加一个数量级，就像急性护理中的深静脉血栓形成(DVT)一样。对于联邦政府正在实施的质量措施，定义和确定队列始终是跟踪和报告的第一步。目前的流程是手动的、有限的和不准确的。通过将来自当前工作流程中创建的临床文档的证据用于实时和基于人群的治疗决策，这种干预将为数据驱动型护理奠定基础，支持改善结果、缩短住院时间和降低直接医疗成本。

This Small Business Innovation Research (SBIR) Phase I project seeks to address the most significant and challenging software need in healthcare: Cohort identification. A cohort is a group of patients with a common medical condition. Cohorts underpin modern medical care, defining treatment algorithms, measuring quality improvement, supporting government initiatives, and representing the core organization for research trials. While manual techniques have been developed to identify a cohort within a healthcare organization's electronic medical record (EMR), all rely on a physician or coder identifying and marking every record for every applicable medical condition. This manual process is inaccurate and only addresses the most common conditions. The suggested novel and revolutionary approach is to use big data techniques, utilizing the detailed unstructured narrative notes recorded on every patient for every encounter in every healthcare institution. The core technology required to extract and make unstructured data usable in healthcare is natural language processing (NLP) combined with coded representations of clinical concepts (ontologies). This proposal brings together industry leading teams and technologies to tackle the greatest data problem in healthcare, which offers a unique opportunity to significantly influence care for decades to come.The broader impact/commercial potential of this project includes creating the foundational infrastructure for the next generation of data-driven healthcare. Just as Google and Yahoo required advanced information extraction and search indexing techniques to make the vast amount of internet data usable, healthcare requires similar enabling technology. The healthcare challenge is even more complex given the multitude of natural language descriptions used by physicians and the complex logic that defines potential cohorts and algorithms. To address these issues, healthcare requires the category of technologies used in Google and Yahoo, but specialized for the healthcare domain. In healthcare, quality improvement requires recognizing at risk cohorts in a population. Missing these cohorts and inadequately treating them can increase mortality by an order of magnitude, as in the case of deep vein thrombosis (DVT) in acute care. For quality measures being implemented by the federal government, defining and identifying cohorts is always the first step of tracking and reporting. Current processes are manual, limited, and inaccurate. By bringing evidence derived from clinical documentation which is created in current workflow to real-time and population based treatment decisions, this intervention will form a foundation for data-driven care, supporting improved outcomes, shorter hospitalizations, and reduced direct medical costs.