Leveraging electronic health records to identify risky alcohol use prior to surgery

利用电子健康记录在手术前识别危险的饮酒情况

• 批准号: 10604757
• 负责人: Anne Christie Fernandez
• 金额: $ 38.98万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604757
• 关键词: Accounting; Address; Adverse event; Agreement; Alcohol abuse; Alcohol consumption; Alcohol withdrawal syndrome; Alcohols; Algorithms; Biological Markers; Classification; Clinic; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Communities; Community Health; Computer Assisted; Consumption; Data; Data Collection; Data Element; Data Set; Early identification; Electronic Health Record; Evaluation; Event; Foundations; Funding; Future; Guidelines; Health; Health Expenditures; Health Services Accessibility; Hospitals; Individual; Institutes; International Classification of Disease Codes; Intervention; Intervention Studies; Label; Lead; Length of Stay; Link; Machine Learning; Measures; Methodology; Methods; Natural Language Processing; Operative Surgical Procedures; Opioid; Outcome; Outcome Study; Patients; Pharmaceutical Preparations; Phase; Phenotype; Postoperative Period; Precision Health; Research; Research Support; Risk; Risk Factors; Severities; Structure; Surgical complication; Testing; Time; Training; Work; alcohol abstinence; alcohol intervention; alcohol risk; alcohol screening; alcohol use disorder; base; care episode; clinical application; cohort; computable phenotypes; computerized tools; cost; direct application; early alcohol use; experience; future implementation; health care service; high risk; hospital readmission; implementation research; improved; innovation; knowledgebase; longitudinal analysis; machine learning method; mortality; novel; phosphatidylethanol; predictive test; prevent; prospective; structured data; surgery outcome; surgical risk; tool; unstructured data

Project Summary/Abstract Patients who consume more than two drinks a day prior to elective surgery are at increased risk of experiencing a myriad of surgical complications, readmissions, and prolonged hospital stays. Fortunately, short-term pre-operative abstinence from alcohol mitigates many surgical risks, and carefully timed interventions can prevent complications and alcohol withdrawal syndrome. However, implementation of pre- operative alcohol interventions requires accurate identification of patients with risky alcohol use at least four weeks prior to surgery. Pre-operative clinics frequently fail to screen for alcohol use or do so too close to the surgery date to allow time for intervention. Electronic health records (EHRs) offer an unprecedented amount of accessible clinical data that can be leveraged to identify risky alcohol use early in the surgical episode of care. Innovative methods are needed to identify data elements and create algorithms to capture risky alcohol use from structured and unstructured EHR data. Natural language processing (NLP) and other machine learning (ML)-based approaches are best suited to extract and analyze alcohol-related clinical narratives, and to synthesize heterogeneous alcohol-related data through computer-assisted methods. The proposed study will leverage EHR data to identify and characterize risky alcohol use among surgical patients to identify cohorts who could benefit from pre-operative alcohol intervention. The study aims are to: 1) develop an electronic, automated computable phenotype to classify risky alcohol use prior to surgery using NLP and ML; 2) validate the algorithm through prospective data collection; and 3) longitudinally evaluate the association between risky alcohol use phenotypes and adverse surgical outcomes including complications and hospital readmissions. Innovative applications of NLP and ML will support evaluation of unstructured EHR data (e.g. clinical notes) and will enable integration of heterogeneous alcohol use data to create the computable phenotype. The aims will be achieved through collaboration of experts in key clinical domains and advanced methodologies. This study will create and validate the first alcohol-specific phenotype-based algorithm for surgical patients, which will support future clinical applications and research into alcohol-related surgical interventions and health outcomes. Study outcomes are expected to have immediate value for identifying cohorts for future implementation research and lead to a new clinical tool for surgical clinics.

项目摘要/摘要 每天在选修手术前每天喝两杯以上饮料的患者的风险增加 经历了无数的手术并发症，再入院和长时间住院。幸运的是， 酒精的短期术前禁欲减轻许多手术风险，并精心定时 干预措施可以预防并发症和戒酒综合征。但是，实施前 手术性酒精干预需要准确鉴定有风险的酒精使用至少四个 手术前几周。术前诊所经常无法筛查酒精使用或太接近 手术日期以允许时间进行干预。电子健康记录（EHRS）提供了前所未有的数量 可以利用的可访问临床数据，可以在外科护理中早期确定危险的酒精使用。 需要创新的方法来识别数据元素并创建算法以捕获风险的酒精使用 来自结构化和非结构化的EHR数据。自然语言处理（NLP）和其他机器学习 基于ML的方法最适合提取和分析与酒精相关的临床叙述，并适合于 通过计算机辅助方法合成异质酒精相关的数据。拟议的研究将 利用EHR数据来识别和表征外科患者中的风险饮酒以识别队列 谁可以从术前的酒精干预中受益。该研究的目的是：1）开发电子， 使用NLP和ML在手术前对危险的饮酒进行分类的自动计算表型； 2）验证 通过潜在的数据收集算法； 3）纵向评估风险之间的关联 酒精使用表型和不良手术结果，包括并发症和医院再入院。 NLP和ML的创新应用将支持对非结构化EHR数据的评估（例如临床注释） 并将能够整合异质性饮酒数据以创建可计算的表型。目的 将通过在关键临床领域和高级方法论中的专家合作来实现。这 研究将创建并验证第一个针对手术患者的基于酒精特异性表型的算法，该算法 将支持未来的临床应用和研究与酒精相关的手术干预和健康的研究 结果。预计研究成果将对未来的同类群体具有直接的价值 实施研究并导致外科诊所的新临床工具。