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

项目摘要/摘要 择期手术前每天饮酒超过两杯的患者患糖尿病的风险增加。 经历了无数的手术并发症、再次住院和长时间的住院治疗。幸运的是， 术前短期戒酒可减轻许多手术风险，并仔细把握时机。 干预可以预防并发症和酒精戒断综合症。然而，实施Pre- 手术酒精干预需要准确识别至少有四次酒精使用风险的患者 手术前几周。术前诊所经常没有对酒精使用进行筛查，或者这样做太接近 手术日期，以便有时间进行干预。电子健康记录(EHR)提供了前所未有的数量 可访问的临床数据，可用于在手术护理的早期识别有风险的酒精使用。 需要创新的方法来识别数据元素并创建算法来捕获有风险的饮酒行为 从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)的方法最适合于提取和分析与酒精相关的临床叙述，以及 通过计算机辅助方法合成不同种类的酒精相关数据。拟议的研究将 利用电子病历数据识别和描述外科患者中有风险的酒精使用情况，以确定队列 谁可以从术前酒精干预中受益。这项研究的目的是：1)开发一种电子、 使用NLP和ML自动计算表型以在手术前对有风险的酒精使用进行分类；2)验证 该算法通过前瞻性数据采集；3)纵向评估风险之间的关联性 酒精使用表型和不良手术结果，包括并发症和再次住院。 NLP和ML的创新应用将支持非结构化电子病历数据(例如临床记录)的评估 并将能够整合不同种类的酒精使用数据，以创建可计算的表型。目标 将通过关键临床领域的专家协作和先进的方法来实现。这 这项研究将为外科患者创建并验证第一个基于酒精特定表型的算法，该算法 将支持未来与酒精相关的外科干预和健康方面的临床应用和研究 结果。研究结果预计将对确定未来的队列具有直接价值 实施研究，为外科临床提供一种新的临床工具。

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.