Leveraging Advanced Informatics to Automate Data Collection of Healthcare Associated Infections (HAI) and Other Surgical Performance Measures

项目摘要 在美国，每年有近5000万人接受手术，大约100万人发展为 严重的并发症，超过15万人在30天内死亡。卫生保健相关感染（HAI）， 手术患者常见的并发症，给患者，他们的家庭，医疗保健带来了巨大的负担 制度，社会。由于减少手术并发症在临床和经济上都是必要的， HAI的有效和准确的报告是至关重要的。不幸的是，目前的HAI报告模式 通常涉及不准确、繁琐和/或昂贵的数据收集。实现准确和 高效自动化的绩效测量数据收集，包括HAI，不仅可以更好地支持 局部手术质量的提高，也将加速从过程型向结果型的转变 适用于基于价值的采购、新的医生支付模式和公开报告的措施。这 一项提案试图通过利用现有的信息技术来应对这一挑战， 自动从HAI和其他手术性能指标的电子健康记录中收集数据。 我们假设，使用先进的信息学方法结合迭代过程， 通过多学科的投入，可以挖掘电子健康记录中的数据，以开发科学有效的，成本低廉的， 有效的结局指标，将与来自ACS NSQIP数据的指标相比， 登记册承认其严格的准确性。我们将在五家独特的医院测试和优化算法， 四个健康系统和两个电子健康记录供应商开始，以确定这一 approach.美国外科医生学会（ACS），主要网站，内容专业知识，临床 登记册和国家影响力，以推动 手术我们已经组建了一个由国家领导人组成的手术结果测量团队， 数据（Clifford Ko，MD，MHS），质量改进和实施科学（Elizabeth Wick，MD，Peter Pronovost，MD，PhD）、信息学（Genevieve Melton，MD，Ph.D.，昆泰）和医院流行病学（Trish Perl，MD，MPH）。成功完成这项工作将在三个主要方面影响该领域：1）提供一个 从EHR自动化五个NQF认可的绩效指标的战略; 2）提供必要的知识 关于在医院层面自动化结果测量和可扩展性的要求， 方法，多个医院，和3）加快过渡，从使用过程的结果措施， 基于价值的采购和医生支付模式。这项研究是新颖和及时的， 这是向所有医院提供有效的外科绩效指标的下一个重要步骤 和临床医生，最终是公众。

PROJECT ABSTRACT Nearly 50 million people have surgery each year in the United States, approximately one million develop serious complications, and over 150,000 die within 30 days. Health-care-associated infections (HAIs), the most common complications in surgical patients, place a huge burden on patients, their families, the healthcare system, and society. As there is a clinical and economic imperative to decrease surgical complications, efficient and accurate reporting of HAIs is paramount. Unfortunately, the current model of HAI reporting routinely involves data collection that is inaccurate, burdensome and/or expensive. Achieving accurate and efficient automation of data collection for performance measures, including HAIs, will not only support better local surgical quality improvement, but also will accelerate the transition from process to outcome-based measures suitable for value-based purchasing, new models of physician payment, and public reporting. This proposal attempts to address this challenge by leveraging currently available informatics technologies to automate the data collection from electronic health records for HAIs and other surgical performance measures. We hypothesize that, using advanced informatics methods combined with an iterative process that is informed by multidisciplinary input, data from electronic health records can be mined to develop scientifically valid, cost- effective outcome measures which will compare favorably with those derived from ACS NSQIP data, a clinical registry recognized for its rigorous accuracy. We will test and optimize the algorithms at five unique hospitals in four health systems and two electronic health record vendors to begin to determine the scalability of this approach. The American College of Surgeons (ACS), the primary site, has the content expertise, clinical registry and national influence necessary to drive change in the approach to performance measurement in surgery. We have assembled an accomplished team of national leaders in surgical outcome measurement and data (Clifford Ko, MD, MHS), quality improvement and implementation science (Elizabeth Wick, MD, Peter Pronovost, MD, PhD), informatics (Genevieve Melton, MD, Ph.D., Quintiles), and hospital epidemiology (Trish Perl, MD, MPH). Successful completion this work will impact the field in three main ways: 1) make available a strategy to automate five NQF-endorsed performance measures from the EHR; 2) provide essential knowledge about what is required at the hospital level to automate outcome measurement and the scalability of this approach to multiple hospitals, and; 3) accelerate transition from the use of process to outcome measures for value-based purchasing and physician payment models. The proposed research is novel and timely, and is the next important step on the path to making valid surgical performance measures available to all hospitals and clinicians, and ultimately the public.