Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States.
Appl Clin Inform. 2018 Apr;9(2):313-325. doi: 10.1055/s-0038-1646963. Epub 2018 May 9.
Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs.
This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs.
ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments.
ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay ( < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge ( < 0.001).
Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.
药物不良反应(ADR)常常未被医务人员识别,降低了患者的生活质量,导致可预防的住院和急诊就诊,并增加了医疗保健成本。
本文评估了一种自动化系统,即药物不良反应识别器(ADER),是否可以帮助临床医生检测和解决住院患者持续的入院前 ADR。
ADER 使用自然语言处理从入院记录中提取患者的药物、检查结果和既往诊断。它将摘录的信息与已知药物不良反应数据库进行比较,并通过在患者的电子健康记录(EHR)中放置警报,及时警告临床医生潜在的持续 ADR 和潜在的混杂因素。一项为期 3 个月的干预试验评估了 ADER 对降压药物开方行为的影响。在患者入院时,范德比尔特大学医院内科病房的医务人员会收到 ADER 关于潜在持续入院前降压药物 ADR 的警告。一个回顾性对照组由干预前一段时间的类似医生组成,他们没有收到警报。该评估比较了两组的开方行为,以确定住院期间或出院时是否更改了入院前药物。该研究还分析了干预组参与者的调查回复和用户评论。
ADER 确定了两组中 30%的潜在入院前 ADR。与对照组相比,干预组的医务人员在住院期间更常停用或停止可疑的引起 ADR 的药物( < 0.001)。对与警报相关的调查做出回应的干预组提供者在出院时更常停用或停止可疑的引起 ADR 的药物( < 0.001)。
结果表明,ADER 帮助医生识别 ADR 并减少了可疑 ADR 药物的开方。在使用 EHR 的医院中,类似于 ADER 的系统可以提高临床医生对持续 ADR 的识别和消除。