权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Strategies for Engineering Reliable Value Sets (SERVS)

工程可靠价值集 (SERVS) 的策略

基本信息

批准号：
10417435
负责人：
ADAM T WRIGHT
金额：
$ 38.67万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10417435
关键词：
Academic Medical Centers Acute myocardial infarction Adrenergic alpha-Antagonists Adrenergic beta-Antagonists Adverse event Ambulances Atenolol Caring Case Series Cessation of life Clinical Code Communities Complement Complex Country Dangerousness Databases Development Effectiveness Electronic Health Record Engineering Epidemiologist Equipment and supply inventories Evaluation Fatigue Funding Health Professional Health system Healthcare Heart Heart Rate Home Hospitalization Hospitals Individual Informatics Intercept Interview Knowledge Lead Left Logic Logical Observation Identifiers Names and Codes Machine Learning Maintenance Measurement Measures Medical Records Methods Mission Myocardial Infarction Ontology Patient Care Patients Pharmaceutical Preparations Plant Roots Population Process Provider Public Health Publishing Pulse Rates Qualitative Methods Research Research Personnel Risk SNOMED Clinical Terms Safety Site Visit Source Suggestion Symptoms System Taxonomy Techniques Technology Time Training Trust United States National Library of Medicine Vendor Visualization Vocabulary Walking Work authority base carvedilol clinical decision support crowdsourcing design detection method experience health care quality health care service organization improved innovation machine learning method method development novel open source tool patient safety prevent success theories tool user centered design

项目摘要

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.

项目总结/文摘