Automated Detection of Anomalous Accesses to Electronic Health Records

自动检测电子健康记录的异常访问

• 批准号: 8882547
• 负责人: Bradley A. Malin
• 金额: $ 0.01万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2016-04-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8882547
• 关键词: Accounting; Administrator; Admission activity; Adopted; Architecture; Attention; Behavior; Businesses; Caring; Case Manager; Case Study; Clinical; Collaborations; Complement; Complex; Computer software; Data; Data Security; Detection; Documentation; Electronic Health Record; Employee; Engineering; Ensure; Entropy; Event; Goals; Grant; Graph; Healthcare; Healthcare Systems; Inpatients; Institution; Interdepartmental Relations; Internet; Lead; Learning; Machine Learning; Measures; Medical; Medical Errors; Medical Records; Medical center; Methods; Mining; Modeling; Molecular; Movement; Neonatal Intensive Care; Neonatology; Pathway interactions; Patient Care; Patients; Pattern; Patterns of Care; Phase; Practice Management; Primary Health Care; Privacy; Process; Productivity; Provider; Recording of previous events; Regulation; Research; Research Personnel; Safety; Semantics; Surface; Surveys; System; Techniques; Technology; Testing; Time; Trust; Variant; Work; base; care delivery; comparative; cost; data mining; design; follow-up; health information technology; infancy; innovation; novel strategies; open source; operation; organizational structure; payment; programs; software systems; ward

DESCRIPTION (provided by applicant): Health information technology (HIT) can lower costs, strengthen productivity, and promote safety. To realize such benefits on a large scale, healthcare organizations (HCOs) are adopting electronic health records (EHRs) to provide various capabilities. Yet, as EHRs and the healthcare workforce grow in diversity, so does their complexity. This is a concern because evidence suggests complex HIT can interrupt care delivery, contribute to medical errors, and expose patient data to privacy breaches. Moreover, such events tend to be discovered only after they transpire en masse, leading to negative media coverage, loss of patients' trust, and sanctions. Federal regulations now enable patients to receive accountings of who accessed their medical records during treatment, payment, and operations related activities. Yet, for patients to make sense of such accountings, they need to be provided with explanations regarding the extent to which accesses are normal in the context of routine HCO activities. We believe that relating specific accesses to patterns of healthcare operations can help explain how medical records are utilized. Unfortunately, many of the aforementioned problems manifest because EHR utilization patterns rarely guide the design and refinement of healthcare management practices. Thus, the overarching objective of our research is to develop novel strategies to automatically learn HCO behavior based on EHR usage. The past several years has witnessed a flurry of activity in this field, but it remains in is infancy and has only scratched the surface of care patterns and the types of anomalies that can be detected. Through this project, we propose to develop anomaly detection methods that integrate the semantics of healthcare operations and allow for the detection of workflows over time. This will enable HCOs and patients to audit in a meaningful way. Moreover, we believe the innovation and dissemination of such data mining strategies will enable HCOs to detect anomalous events that indicate system misuse and patients who require special attention, but also effectively audit business practices and discover inefficient workflows. The specific aims of this project are (1) to develop machine learning approaches, based on intrasession utilization patterns, to streamline EHR interface configuration and detect anomalous sessions, (2) to design a data mining framework, based on intersession EHR access patterns, to characterize HCO departmental interactions in patient treatment and detect anomalous events, and (3) to infer patient management pathways to consolidate redundant processes and detect deviations from anticipated workflows. In support of these goals, we will evaluate, compare, and contrast the workflows and anomalies in the EHR systems of two large medical centers. Additionally, we will ensure that our methods are integrated into an open source software system that can assist HCOs to extract, transform, and load (ETL) access data from EHRs, analyze such data for anomalies, and visualize the results in interfaces that enable review by healthcare administrators and patients. In doing so, we will be able to compare and contrast behavior of the workflows and multiple institutions and develop methods that appropriately generalize across EHR systems.

描述（由申请人提供）：健康信息技术（HIT）可以降低成本，增强生产率并促进安全性。为了大规模实现此类好处，医疗组织（HCO）正在采用电子健康记录（EHR）来提供各种功能。然而，随着EHRS和医疗保健劳动力的多样性增长，其复杂性也是如此。这是一个问题，因为有证据表明复杂的命中可以中断护理，导致医疗错误并将患者数据暴露于隐私漏洞中。此外，此类事件只有在大规模散发出来后才会发现这些事件，从而导致负面的媒体报道，丧失患者的信任和制裁。现在，联邦法规使患者能够在治疗，付款和与操作相关的活动期间获得谁访问其病历的人。但是，要使患者理解此类会计，需要为他们提供有关在常规HCO活动中访问正常的程度的解释。我们认为，将特定访问与医疗保健操作模式相关联可以帮助解释如何利用医疗记录。不幸的是，上述许多问题出现了，因为EHR利用模式很少指导医疗保健管理实践的设计和完善。因此，我们研究的总体目标是制定新的策略，以根据EHR使用自动学习HCO行为。过去的几年目睹了这一领域的一系列活动，但它仍然处于婴儿期，并且只刮擦了可以检测到的护理模式的表面和异常类型。通过这个项目，我们建议开发呼吸检测方法，以整合医疗保健操作的语义，并允许随着时间的推移检测工作流程。这将使HCO和患者能够以有意义的方式进行审核。此外，我们认为，此类数据挖掘策略的创新和传播将使HCO能够检测出异常事件，这些事件表明系统滥用和需要特别注意的患者，并且有效地审核业务实践并发现效率低下的工作流程。该项目的具体目的是（1）基于内包的利用模式开发机器学习方法，以简化EHR接口配置并检测异常会话，（2）设计基于间隔EHR访问模式的数据挖掘框架，以在患者治疗和探讨患者的范围​​和探讨态度和（3）范围内的疗程和（3）的患者中（3），以表征异常的病人和（3）。偏离预期工作流程。为了支持这些目标，我们将评估，比较和对比两个大型医疗中心的EHR系统中的工作流和异常。此外，我们将确保将我们的方法集成到开源软件系统中，该系统可以帮助HCO从EHRs提取，转换和加载（ETL）访问数据，分析异常数据，并在界面中可视化结果，从而使医疗保健管理员和患者进行审查的接口。通过这样做，我们将能够比较工作流和多个机构的对比行为，并开发在EHR系统中适当推广的方法。