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Appl Clin Inform 2018; 09(02): 313-325
DOI: 10.1055/s-0038-1646963
Research Article
Schattauer GmbH Stuttgart

Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions

Joshua C. Smith
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Qingxia Chen
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
2   Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Joshua C. Denny
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Dan M. Roden
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
4   Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Kevin B. Johnson
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
5   Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Randolph A. Miller
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
6   School of Nursing, Vanderbilt University, Nashville, Tennessee, United States
› Author Affiliations Funding This research was supported in part by AHRQ Health Services Dissertation Grant R36HS023485, National Library of Medicine (NLM) Training Grant (T15LM007450), NLM R01LM007995 and R01LM010828. Resources provided by the Vanderbilt Institute for Clinical and Translational Research (VICTR), supported by CTSA award UL1TR000445 from the National Center for Advancing Translational Sciences, also benefitted the study. The AHRQ study section reviewed the preliminary study design as part of making its funding decision. Funding sources had no role in the design, implementation, or interpretation of this study or the decision to submit the manuscript for publication.
Further Information

Publication History

17 October 2017

22 March 2018

Publication Date:
09 May 2018 (online)

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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.

Keywords

adverse drug events - natural language processing - clinical decision support systems

Authors' Contributions

All authors contributed to the study design. J.C.S. and R.A.M. wrote the study protocols, acquired IRB approval, and recruited participants. J.C.S. performed all software development, debugging, data acquisition, and data processing under the supervision of R.A.M. With the assistance of K.B.J., R.A.M., and technical staff (see Acknowledgments), J.C.S. integrated the ADER system into the EHR and maintained the system during the study. J.C.S. and Q.C. performed the statistical analysis. J.C.S. and R.A.M. interpreted the results and wrote the first drafts of the manuscript. Q.C., J.C.D., D.M.R., and K.B.J. provided critical comments, suggestions, and changes to the manuscript. All authors approved the final manuscript.


Protection of Human and Animal Subjects

This study was performed in compliance with all applicable ethical standards for medical research involving human subjects. The Vanderbilt University Institutional Review Board approved this study (IRB #141341).


Supplementary Material

 

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