Paper Info
Title
Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions.
Abstract
Background 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. Objective This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs. Methods 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. Results 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 (p < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge (p < 0.001). Conclusion 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.
Year
DOI
Venue
2018
10.1055/s-0038-1646963
APPLIED CLINICAL INFORMATICS
Keywords
DocType
Volume
adverse drug events,natural language processing,clinical decision support systems
Journal
9
Issue
ISSN
Citations 
2
1869-0327
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
Joshua C. Smith162.28
Qingxia Chen2153.70
Joshua C. Denny393297.43
Dan M. Roden417718.14
Kevin B. Johnson501.35
Randolph A. Miller6306151.21