Literature DB >> 23114393

Adverse-drug-event surveillance using narrative nursing records in electronic nursing records.

Hee-Jung Ahn1, Hyeoun-Ae Park.   

Abstract

The purpose of this study was to determine whether the frequency of adverse drug events can be extracted by analyzing narrative nursing statements documented in standardized terminology-based electronic nursing records. For this study, we reviewed the narrative nursing documentations of 487 admissions of 355 cancer patients who were treated with cisplatin at a tertiary-care hospital in Korea. Narrative nursing statements with the terms "adverse drug reaction," "allergy," "hypersensitivity," and other adverse drug events listed in the safety information were analyzed. In addition, nausea, one of the most frequent adverse drug events, was further examined. Narrative statements documenting the presence or absence of an "adverse drug reaction," "allergy," and "hypersensitivity" were found in 162 admissions (33.3%). The presence or absence of adverse drug events due to cisplatin was documented in 476 admissions (97.7%). At least one adverse drug event was noted in 258 admissions (53.0%). The presence of nausea was documented in 214 admissions (43.9%), and the mean duration of nausea was 5.2 days. The results of this study suggest that adverse drug events can be monitored using narrative nursing statements documented in standardized terminology-based electronic nursing records.

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Year:  2013        PMID: 23114393     DOI: 10.1097/NXN.0b013e318270106e

Source DB:  PubMed          Journal:  Comput Inform Nurs        ISSN: 1538-2931            Impact factor:   1.985


  4 in total

1.  Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance.

Authors:  Suehyun Lee; Jongsoo Han; Rae Woong Park; Grace Juyun Kim; John Hoon Rim; Jooyoung Cho; Kye Hwa Lee; Jisan Lee; Sujeong Kim; Ju Han Kim
Journal:  Drug Saf       Date:  2019-05       Impact factor: 5.606

2.  Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records.

Authors:  Suehyun Lee; Jiyeob Choi; Hun-Sung Kim; Grace Juyun Kim; Kye Hwa Lee; Chan Hee Park; Jongsoo Han; Dukyong Yoon; Man Young Park; Rae Woong Park; Hye-Ryun Kang; Ju Han Kim
Journal:  J Am Med Inform Assoc       Date:  2017-07-01       Impact factor: 4.497

3.  Detection of unknown ototoxic adverse drug reactions: an electronic healthcare record-based longitudinal nationwide cohort analysis.

Authors:  Suehyun Lee; Jaehun Cha; Jong-Yeup Kim; Gil Myeong Son; Dong-Kyu Kim
Journal:  Sci Rep       Date:  2021-07-07       Impact factor: 4.379

4.  Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning.

Authors:  Eunjoo Jeon; Youngsam Kim; Hojun Park; Rae Woong Park; Hyopil Shin; Hyeoun-Ae Park
Journal:  Healthc Inform Res       Date:  2020-04-30
  4 in total

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