Strategies for Engineering Reliable Value Sets (SERVS)

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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.