Strategies for Engineering Reliable Value Sets (SERVS)

项目摘要/摘要 有重要证据表明，当有效使用CDS时，可以提高卫生保健质量、安全性和 和有效性。然而，尽管CDS具有潜力，但由于不良反应，CDS可能会导致重大不良事件。 功能。在我们之前的工作中，我们发现CDS故障的一个重要来源与 与维护准确和一致的值集有关的问题。值集合是“代码和对应的列表” 来自NLM托管的标准临床词汇表(如SNOMED CT®、RxNorm、 LOINC®和其他)，它们定义了临床概念。它们通常用于两种临床决策 支持和临床质量测量(CQM)以定义复杂的概念。创建和维护 值设置本身就具有挑战性，而值设置错误可能会导致CDS和质量方面的错误 测量。我们发现，这些错误是普遍存在的，原因是多方面的。在 马尔代夫(机器学习驱动的交互式价值集增强系统)项目，我们提出了 第一个全面研究价值集的创造和维护，小说和小说的发展-- 创新的机器学习和基于本体的方法，用于改进值集，并创建 新的开源工具，帮助设置作者和用户的价值。马尔代夫依赖于定性和定量的混合 方法，开发新的本体和基于机器学习的方法来提高价值 套装和新的开源工具，我们预计它将产生新的理论，临床应用的创新- 机器学习的应用，以及为价值集作者和用户提供的实用工具和过程。这些 进展将改善临床决策支持和质量测量，减少警觉疲劳和 向患者安全和医疗质量的改善致敬。

Project Summary/Abstract Significant evidence suggests that CDS, when used effectively, can improve health care quality, safety, and effectiveness. However, despite its potential, CDS can cause significant adverse events due to mal- functions. In our previous work, we identified that a significant source of CDS malfunctions is related to problems with maintaining accurate and consistent value sets. Value sets are “lists of codes and corre- sponding terms, from NLM-hosted standard clinical vocabularies (such as SNOMED CT®, RxNorm, LOINC® and others), that define clinical concepts.” They are commonly used in both clinical decision support and clinical quality measures (CQMs) to define complex concepts. Creating and maintaining value sets is inherently challenging, and value set errors can lead to errors in both CDS and quality measurement. We have found that these errors are widespread and have a variety of causes. In the MALDIVES (Machine Learning-Driven Interactive Value Set Enhancement System) project, we propose the first comprehensive study of value set creation and maintenance, the development of novel and inno- vative machine learning and ontology-based approaches for improving value sets, and the creation of new, open-source tools to help value set authors and users. MALDIVES relies on a mix of qualitative methods, development of novel ontology and machine learning-based methods for improvement of value sets, and new open-source tools, and we anticipate that it will yield new theory, innovations in clinical ap- plications of machine learning, and practical tools and processes for value set authors and users. These advances will improve clinical decision support and quality measurement, reduce alert fatigue and con- tribute to improvements in patient safety and healthcare quality.