SBIR Phase II: Applying Language Understanding at the Point of Care to Enhance Clinical Documentation and Realize Quality Improvements

SBIR 第二阶段：在护理点应用语言理解来增强临床记录并实现质量改进

• 批准号: 1330136
• 负责人: Raj Tiwari
• 金额: $ 40.57万
• 依托单位: Health Fidelity, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330136&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Applying; Language

The innovation presented in this proposal is a new paradigm for capturing critical quality information at the point-of-care using advanced technologies in an intuitive workflow. Healthcare quality and cost represent top national priorities. An increasingly common strategy to improve outcomes and value of care is performance assessment to promote best practices. Over decades, the most successful quality improvement (QI) programs have been heavily data-reliant. These programs require identifying patients that fit specific quality measures and assuring their care meets national guidelines. Linking patients to quality measures is the rate limiting step, involving an overwhelming amount of manual labor to review narrative notes one at a time and link them to an appropriate subset of hundreds of known quality measures. Leveraging a robust platform proven in Phase I, the proposed Phase II solution offers an automated approach to capturing a set of quality measures in real-time. The output will provide rich and compliant documentation enhanced with quality measures that feed the electronic health record (EHR) and downstream clinical, operational, and financial hospital systems through standard protocols. The goal is a disruptive change that will fast-track national initiatives and enable a safer and more efficient healthcare system.The broader/commercial impact of this program is to further national healthcare goals of reducing cost and improving quality in care. The approach leverages increased breadth, depth, and accuracy of patient data captured at the point of care. The most aggressive national initiatives encourage capturing a small portion of the hundreds of known quality measures. Accelerating capture and use of quality measures is an opportunity to meaningfully improve a healthcare system that lags in quality and cost. Impact must also be considered at a personal level. There is a cost to care within a system where quality is not documented and tracked. A typical example out of the hundreds of defined measures is ventilator associated pneumonia (VAP). Multiple studies on manual programs to document VAP and leverage care algorithms demonstrate greater than 40% reduction in mortality and 20% reduction in cost. VAP, though common, did not make the top 15 list of measures required by the government in 2014 because it is too difficult to capture. There is currently no automated approach to capture this quality measure. Addressing technical limitations in documenting quality measures will expand QI reach and save lives.

该提案中提出的创新是在直观的工作流程中使用高级技术在护理点捕获关键质量信息的新范例。医疗质量和成本是国家的首要任务。改善护理成果和价值的一个日益普遍的战略是进行绩效评估，以促进最佳做法。几十年来，最成功的质量改进(QI)计划一直严重依赖数据。这些计划要求确定符合特定质量标准的患者，并确保他们的护理符合国家指南。将患者与质量指标联系起来是速度限制步骤，需要大量的体力劳动，一次审阅一个叙述性笔记，并将它们链接到数百个已知质量指标的适当子集。利用在第一阶段经过验证的强大平台，建议的第二阶段解决方案提供了一种自动方法来实时捕获一组质量衡量标准。输出将提供丰富和合规的文档，并通过标准协议提供电子健康记录(EHR)和下游临床、运营和财务医院系统的质量措施。目标是一项颠覆性的变化，将快速跟踪国家倡议，并实现更安全、更高效的医疗体系。该计划的更广泛/更商业影响是促进降低成本和提高医疗质量的国家医疗目标。该方法利用了在护理点捕获的患者数据的更大广度、深度和准确性。最激进的国家倡议鼓励捕获数百项已知质量指标中的一小部分。加快质量措施的获取和使用，是有意义地改善在质量和成本方面落后的医疗体系的机会。影响也必须在个人层面上考虑。在一个没有记录和跟踪质量的系统中，护理是有成本的。在数百项已定义的措施中，一个典型的例子是呼吸机相关性肺炎(VAP)。对记录VAP和利用护理算法的手动程序的多项研究表明，死亡率降低了40%以上，成本降低了20%。VAP虽然很常见，但没有进入2014年政府要求的15项措施之列，因为它太难捕捉了。目前还没有自动化的方法来获取这一质量衡量标准。解决记录质量措施的技术限制将扩大QI覆盖范围并拯救生命。