Patient Safety Event Surveillance Using Machine Learning and Free Text Clinical Notes

项目总结/摘要 拟议的项目旨在通过收集以患者为中心的 结果作为输入数据，以支持学习健康的安全和改进模型 系统（LHS）。该项目将通过利用现有的机器学习来实现这些目标 对自由文本文档（例如，临床医生笔记）进行分类的方法， 感兴趣的具体事件。该项目有两个长期目标。 第一个广泛的项目目标是收集重要的数据，以解决知识差距， 5例重症儿科住院患者医疗获得性疾病发生率及临床流行病学分析 条件（HAC）。这5种HAC是：外周静脉浸润、静脉血栓栓塞 （VTE）、压力损伤、患者福尔斯跌倒以及涉及对提供者造成伤害的事件。 第二个目标是评估一种新的常规患者安全事件监测方法， 是可扩展的，可转移的，适应其他条件和设置，并具有低成本 可持续的持续经营。为实现这些目标，该项目有两个具体目标： 目标1：加强对5例儿科HAC的监测。比较特性 以前和新发现的病例。描述高危人群。 目标2：评估现有系统的完整性。评估增强的效果 监察质素改善活动;医院中毒个案的发生率;以及运作成本 系统，包括工作人员的时间和资源。 项目团队开发了一个以开放许可证实现的机器学习接口 Windows软件。该团队有一个漫长的跟踪记录，使这些方法可以访问， 研究、临床操作、质量改进和伤害方面的临床医生和非专业用户 预防设置。目前的项目提出了一个创新的应用这些 技术，方法和工具，病人安全监测的重要问题。 该项目的预期成果将是对这5个国家中每一个国家的知识的实质性进步。 建议加强对儿科HAC的监测。结果将根据现有的 数据完整性和临床流行病学。调查结果将直接解决对 现有数据源的局限性，从而推动患者安全改善活动。 另一个预期的结果将是对一种新的治疗患者的方法进行严格的评估。 安全监督。这将包括分析加强监测的成本和效益 机器学习与当前方法的对比，以及方法的成本效益 与依赖现有数据和合作伙伴社区医院的外部验证相比。

PROJECT SUMMARY/ABSTRACT The proposed project aims to make healthcare safer through collection of patient-centered outcomes as the input data to support a safety and improvement model of the Learning Health System (LHS). The project will accomplish these aims by leveraging existing machine learning methods to classify free text documents, such as clinician notes, for the presence or absence of specific events of interest. The project shares this focus with two long-term objectives. The first broad project goal is to collect important data to address knowledge gaps in the incidence and clinical epidemiology of 5 serious pediatric inpatient healthcare acquired conditions (HACs). These 5 HACs are: peripheral IV infiltrates, venous thromboembolisms (VTEs), pressure injuries, patient falls, and incidents involving harm to providers. The second goal is to evaluate a novel approach to routine patient safety event surveillance that is scalable, transferrable, adaptable to other conditions and settings, and with low cost of sustainable ongoing operation. The project has two specific aims to achieve these goals: Aim 1: Implement enhanced surveillance for 5 pediatric HACs. Compare characteristics of previously and newly identified cases. Describe high-risk populations. Aim 2: Estimate completeness of existing systems. Evaluate effects of enhanced surveillance on quality improvement activities; incidence of HACs; and cost to operate system, including staff time and resources. The project team has developed a machine learning interface implemented in open license Windows software. The team has a lengthy track record making these methods accessible to clinicians and lay users in research, clinical operations, quality improvement, and injury prevention settings. The current project proposes an innovative application of these technologies, methods, and tools to the important problem of patient safety surveillance. An expected outcome of this project will be substantial advance in knowledge for each of the 5 pediatric HACs proposed for enhanced surveillance. Results will be reported in terms of existing data completeness and clinical epidemiology. Findings will directly address concerns over limitations of existing data sources and thereby drive patient safety improvement activities. An additional expected outcome will be the rigorous evaluation of a novel approach to patient safety surveillance. This will include analysis of the costs and benefits of enhanced surveillance with machine learning versus current approaches, and the cost-effectiveness of the approach compared to reliance on existing data, and external validation at a partner community hospital.