Automated Surveillance of Postoperative Infections (ASPIN)

项目总结/摘要 我们的长期目标是减少术后感染。我们将从开发一个系统开始， 通过将机器学习算法应用于电子健康记录， (EHR)数据我们将利用全面的审计和反馈系统来创建风险调整后的利率报告 以及与外科医生和其他医生共享的术后感染并发症的具体细节 卫生保健提供者，以促进他们的认识。我们称之为自动监控系统 术后感染（ASPIN）。ASPIN将在北大四大医院试点 科罗拉多卫生系统（UCHealth），合并手术量约为80，000例患者/ 年我们希望这将取代昂贵和费力的人工部分采样的术后 感染并发症，目前许多医院使用。 具体目标1。扩大和加强术前风险预测和术后识别的模型 使用EHR和ACS NSQIP数据的外科感染患者在4岁时接受手术， UCHealth医院 具体目标1a）通过以下方式增强先前开发的用于识别术后感染的模型： 通过“仿制品”控制I型错误，这是最近用于高维模型选择的统计创新 使用错误发现率校正。 具体目标1b）使用这些患者的EHR文本报告部署自然语言处理方法， 确定术后感染的其他指标，并进一步完善模型。 具体目标1c）使用EHR数据创建术前感染风险模型-类似于模型 在AHRQ资助的手术风险术前评估系统中实施-但不要求 医疗保健提供者的额外数据输入。 具体目标2。从研究一开始，就利用咨询委员会的投入制定ASPIN 由加州大学健康中心四家医院的管理人员和外科医生组成。外科医生的其他反馈 在ASPIN开发的几个步骤中，将通过焦点小组和半结构化访谈获得 和实施规划。 具体目标3。ASPIN的试点实施将利用RE-AIM框架来指导和检查 ASPIN在UCHealth的初步有效性和可行性。我们将招募30名外科医生参与， 四家UCHealth医院使用ASPIN，我们将评估其覆盖范围，有效性，采用率， 实施ASPIN。 这项研究响应AHRQ的优先事项，利用现有的数据来开发一个学习健康系统 重点是改善术后医疗相关感染的监测和报告。

PROJECT SUMMARY/ABSTRACT Our long term goal is to reduce postoperative infections. We will start by developing a system to accurately and completely identify their occurrence by applying machine learning algorithms to electronic health record (EHR) data. We will utilize a comprehensive audit and feedback system to create reports of risk-adjusted rates and specific details of postoperative infectious complications that are shared with surgeons and other healthcare providers to facilitate their awareness. We call this system the Automated Surveillance of Postoperative Infections (ASPIN). ASPIN will be piloted in the four major hospitals of the University of Colorado Health system (UCHealth) with a combined surgical volume of approximately 80,000 patients per year. We expect this will supersede the costly and laborious manual partial sampling of postoperative infectious complications which is current utilized by many hospitals. Specific Aim 1. Expand and enhance models for preoperative risk prediction and postoperative identification of surgical infections using EHR and ACS NSQIP data from patients who underwent operations at four UCHealth hospitals. Specific Aim 1a) Enhance previously-developed models for identification of postoperative infections by controlling Type-I errors via “knockoffs,” a recent statistical innovation for high dimensional model selection using false discovery rate correction. Specific Aim 1b) Deploy natural language processing methods using EHR text reports of these patients to identify additional indicators of postoperative infections and further refine the models. Specific Aim 1c) Create preoperative risk models for infection using EHR data - similar to the models implemented in the AHRQ-funded Surgical Risk Preoperative Assessment System - but that do not require additional data entry by the health care providers. Specific Aim 2. From the beginning of the study, develop ASPIN with input from an Advisory Committee composed of administrators and surgeons from all four UCHealth hospitals. Additional feedback from surgeons will be obtained through focus groups and semi-structured interviews at several steps of ASPIN development and implementation planning. Specific Aim 3. A pilot implementation of ASPIN will utilize the RE-AIM framework to guide and examine the preliminary effectiveness and feasibility of ASPIN at UCHealth. We will recruit 30 surgeon participants from all four UCHealth hospitals to use ASPIN, and we will evaluate the reach, effectiveness, adoption, and implementation of ASPIN. This research responds to AHRQ priorities by utilizing existing data to develop a learning health system with a distinct focus on improving surveillance and reporting of postoperative healthcare-associated infections.