Health Information Technology for Surveillance of Health Care-Associated Infections

用于监测医疗保健相关感染的健康信息技术

• 批准号: 10186798
• 负责人: BRIAN T BUCHER
• 金额: $ 16.01万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186798
• 关键词: Health; Information; Technology; Surveillance; Care

PROJECT SUMMARY/ABSTRACT Health care-associated infections (HAIs) affect one in every 20 hospitalized patients and account for $10 billion dollars in potentially preventable health care expenditures annually. Current efforts at detection of HAIs are limited to manual chart review which hinders the generalizability and scalability of HAI detection. My goal in seeking a Mentored Clinical Scientist Career Development Award is to acquire the necessary training, practical experience, and knowledge to develop a health services research career as a principal investigator focusing on leveraging novel health information technology (HIT) tools to improve the measurement of surgical health care quality, safety, and effectiveness. To continue my progress towards this goal, the objective of this project is to address the challenges of HAI detection by developing a robust and portable automated HAI surveillance toolkit. This toolkit will combine structured electronic health record (EHR) data with rich information locked in clinical notes using machine learning and natural language processing (NLP) to identify HAIs after surgical procedures. Our overall hypothesis is that combining structured variables from the EHR supplemented with NLP will improve our ability to identify HAIs after surgical procedures. To test the central hypothesis and accomplish the objectives for this application, I will pursue the following three specific aims: 1) Determine the EHR data elements indicative of postoperative HAIs and evaluate the performance of a novel HAI surveillance algorithm; 2) Identify the presence of postoperative SSIs from clinical notes using an automated portable NLP-based algorithm; 3) Apply user-centered design to create a high fidelity prototype of a surgical quality dashboard incorporating our HAI case detection methodology. This contribution is a significant first step in a continuum of research that utilizes the large amounts of data in the EHR combined with novel HIT methods to improve the measurement of surgical health-care quality, safety, and effectiveness. This approach is significant because the tools developed in this proposal have potential to serve as a prototype for identification and monitoring hospitals adverse events and could be replicated on a national scale. The proposed research is innovative in its approach using a combination of structured and unstructured data in the EHR along with novel machine learning and NLP tools to create a generalizable surveillance toolkit for the detection of HAIs. This proposal is responsive to the AHRQ Special Emphasis Notice (NOT-HS-13-011) specifically addressing the use of HIT to improve quality measurement. I have assembled a mentoring team who all internationally recognized experts with long and successful track records of funding and trainee mentorship. This project will provide the means to place me on a trajectory towards a health services research career focused on improving the measurement of surgical health-care quality, safety, and effectiveness using novel HIT tools. In summary, my previous training and experience, innovative research plan, high-quality training plan, first-rate mentorship team, and supportive research environment give me the highest likelihood of success to research independence with the proposed K08 award.

项目摘要/摘要 卫生保健相关感染(HAI)影响每20名住院患者中的一名，占100亿美元 每年潜在的可预防的医疗保健支出。目前在检测禽流感病毒方面所做的努力是 局限于人工审图，影响了HAI检测的通用性和可扩展性。我的目标是 寻求导师临床科学家职业发展奖是为了获得必要的培训，实用 以首席研究员的身份发展卫生服务研究事业的经验和知识，重点是 利用新的健康信息技术(HIT)工具改进外科健康护理的衡量标准 质量、安全和有效性。为了继续朝着这个目标前进，这个项目的目标是 通过开发强大和便携的自动化HAI监视工具包来应对HAI检测的挑战。 该工具包将结构化电子健康记录(EHR)数据与锁定在临床中的丰富信息相结合 注：使用机器学习和自然语言处理(NLP)来识别手术后的HAI。 我们的总体假设是，将来自EHR的结构变量与NLP相结合将会改善 我们在手术后识别血吸虫病的能力。检验中心假设，实现目标 对于这个应用程序，我将追求以下三个具体目标：1)确定指示的EHR数据元素 并评估一种新的HAI监测算法的性能；2)识别 使用基于自动便携NLP的算法从临床记录中发现术后SSI；3)适用 以用户为中心的设计，创建包含我们的HAI的手术质量仪表板的高保真原型 病例检测方法。这一贡献是一系列研究的重要的第一步，该研究利用 电子病历中的大量数据与新的HIT方法相结合提高了手术测量的质量 医疗保健质量、安全性和有效性。这种方法很重要，因为在本系列中开发的工具 建议有可能成为识别和监测医院不良事件和 可以在全国范围内复制。拟议的研究在方法上是创新的，使用了 电子病历中的结构化和非结构化数据，以及新的机器学习和NLP工具，以创建 用于检测禽流感病毒的通用监测工具包。这项建议是对AHRQ特别计划的回应 强调通知(NOT-HS-13-011)专门针对使用HIT来改进质量测量。我 我组建了一支指导团队，所有国际公认的专家都有漫长而成功的轨迹 资助记录和实习生指导记录。这个项目将提供手段，让我走上一条通往 卫生服务研究生涯专注于改善外科卫生保健质量、安全性、 以及使用新的HIT工具的有效性。综上所述，我以前的培训和经验，创新性的研究 计划、高质量的培训计划、一流的指导团队和支持性的研究环境给了我 通过建议的K08奖，成功研究独立性的可能性最高。