EHR-based patient safety: Automated error detection in neonatal intensive care

描述(申请人提供)：在新生儿患者安全领域，缺乏系统性研究是取得进展的关键障碍。值得注意的是，缺少仔细调查电子健康记录(EHR)和信息技术检测新生儿重症监护相关错误的研究。国家儿童健康与人类发展研究所(NICHHD)的专家小组发现，目前对新生儿患者安全研究的了解存在多个空白。拟议的工作是对资助机会公告的三个方面的重点回应：1.开发前瞻性和回溯性研究设计，以收集有关患者安全和不良事件的数据。2.研究当前错误报告系统方法的优点和局限性。3.研究商业IT系统和EHR在减少医疗差错方面的有用性。在我们的研究中，我们试图将患者安全研究转向自动化和计算机化的方法，以实现更全面的患者安全范例。我们将开发新的基于内容的电子健康记录(EHR)自动化算法，这些算法对患者安全研究是新的，以1)检测错误(目标1)和2)对后续伤害进行分类(目标2)。临床自然语言处理(NLP)领域的最新信息提取和统计分类技术将适应患者安全研究任务。在目标1中，我们将在全国最大的新生儿重症监护病房(NICU)之一对700张图表(我们机构一整年的患者入院情况)实施有重点的手动审查，以填补文献中的空白。使用触发工具，我们将识别发生在三个特定类别中的错误-实验室测试错误、药物/液体错误和呼吸道管理错误。我们将开发基于电子病历的错误自动检测的新算法，并对照人类图表审查员的触发工具审查(当前的黄金标准)和自愿事件报告系统(公认标准)的性能来评估新算法的性能。在目标2中，我们将研究基于电子病历的新型信息提取和统计算法的效用，以根据所产生的危害程度对错误进行自动分类。我们提出的工作有可能实现新生儿患者安全研究和实践方法的范式转变。这项研究是实现大规模患者安全监测自动化的根本步骤，并改善每年数百万儿童在NICU中的差错识别和患者安全。 与公共卫生相关：我们正在开发一种自动错误检测技术，以提高新生儿在医院护理期间的安全性。我们的工作是首次尝试在电子健康记录中大规模使用文本分析，以降低成本，同时提高新生儿临床护理中错误检测的速度和全面性。

DESCRIPTION (provided by applicant): In the field of neonatal patient safety, the paucity of systematic research is a critical barrier to progress. Notably missing are studies that meticulously investigate Electronic Health Records (EHR) and information technology in detecting neonatal intensive care-related errors. The expert panel at the National Institute of Child Health and Human Development (NICHHD) identified multiple gaps in the current knowledge of neonatal patient safety research. The proposed work is a well focused response to three dimensions of the Funding Opportunity Announcement: 1.Develop prospective and retrospective study designs to collect data on patient safety and adverse events. 2.Study the strength and limitations of current methods of error reporting systems. 3.Study the usefulness of commercial IT systems and EHRs in reducing medical errors. In our study we seek to shift patient safety research toward an automated and computerized approach to achieve a more comprehensive patient safety paradigm. We will develop novel Electronic Health Record (EHR) content-based automated algorithms that are new to patient safety research to 1) detect errors (Aim 1) and 2) categorize subsequent harm (Aim 2). State of the art information extraction and statistical classification techniques from the field of clinical Natural Language Processing (NLP) will be adapted to the patient safety research tasks. In Aim 1 we will fill the gap in the literatre by implementing a focused manual review of 700 charts (one full year of patient admissions at our institution) in one of the largest Neonatal Intensive Care Units (NICU) in the nation. Using a trigger tool, we will identify errors occurring in three specified categories - laboratory test errrs, medication/fluid errors, and airway management errors. We will develop novel algorithms for automated EHR-based detection of the errors and evaluate the performance of the new algorithms against the performance of both trigger tool review by human chart reviewers (current gold standard) and the voluntary incident reporting system (accepted standard). In Aim 2, we will study the utility of novel EHR-based information extraction and statistical algorithms for the automated categorization of errors according to the resulting level of harm. Our proposed work has the potential to accomplish a paradigm shift in the methods of neonatal patient safety research and practice. The study is a fundamental step to automating patient safety monitoring on a large scale and improving error identification and patient safety in NICUs for millions of children every year. PUBLIC HEALTH RELEVANCE: We are developing an automated error detection technique to improve the safety of newborn babies during hospital care. Our work is the first known attempt to use text analysis in the electronic health records on a large scale to reduce the cost while at the same time increase the speed and comprehensiveness of error detection in the clinical care of newborns.