Sinha Shyamashree, Burstein Gale R, Leonard Kenneth E, Murphy Timothy F, Elkin Peter L
Department of Biomedical Informatics, University at Buffalo - SUNY, Buffalo, New York, USA.
Erie County Department of Health, Buffalo, New York, USA.
Stud Health Technol Inform. 2017;245:594-598.
Opioid dependence and overdose is on the rise. One indicator is the increasing trends of prescription buprenorphine use among patient on chronic pain medication. In addition to the New York State Department of Health's prescription drug monitoring programs and training programs for providers and first responders to detect and treat a narcotic overdose, further examination of the population may provide important information for multidisciplinary interventions to address this epidemic. This paper uses an observational database with a Natural Language Processing (NLP) based Not Only Structured Query Language architecture to examine Electronic Health Record (EHR) data at a regional level to study the trends of prescription opioid dependence. We aim to help prioritize interventions in vulnerable population subgroups. This study provides a report of the demographic patterns of opioid dependent patients in Western New York using High Throughput Phenotyping NLP of EHR data.
阿片类药物依赖和过量使用情况呈上升趋势。一个指标是慢性疼痛药物患者中处方丁丙诺啡使用量的增加趋势。除了纽约州卫生部的处方药监测计划以及针对提供者和急救人员的检测和治疗麻醉品过量的培训计划外,对该人群的进一步研究可能会为应对这一流行病的多学科干预措施提供重要信息。本文使用一个基于自然语言处理(NLP)的非结构化查询语言架构的观测数据库,在区域层面检查电子健康记录(EHR)数据,以研究处方阿片类药物依赖的趋势。我们旨在帮助确定弱势群体亚组干预措施的优先顺序。本研究使用EHR数据的高通量表型NLP,提供了纽约西部阿片类药物依赖患者的人口统计学模式报告。