Literature DB >> 14552851

Assessing explicit error reporting in the narrative electronic medical record using keyword searching.

Hui Cao1, Peter Stetson, George Hripcsak.   

Abstract

BACKGROUND: Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record.
METHOD: We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. RESULT: We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors.
CONCLUSION: Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.

Entities:  

Mesh:

Year:  2003        PMID: 14552851     DOI: 10.1016/s1532-0464(03)00058-3

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  6 in total

1.  Automated detection of adverse events using natural language processing of discharge summaries.

Authors:  Genevieve B Melton; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

2.  Evaluating the state-of-the-art in automatic de-identification.

Authors:  Ozlem Uzuner; Yuan Luo; Peter Szolovits
Journal:  J Am Med Inform Assoc       Date:  2007-06-28       Impact factor: 4.497

3.  Automatic extraction and assessment of lifestyle exposures for Alzheimer's disease using natural language processing.

Authors:  Xin Zhou; Yanshan Wang; Sunghwan Sohn; Terry M Therneau; Hongfang Liu; David S Knopman
Journal:  Int J Med Inform       Date:  2019-08-06       Impact factor: 4.046

4.  An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes.

Authors:  Foster R Goss; Joseph M Plasek; Jason J Lau; Diane L Seger; Frank Y Chang; Li Zhou
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

5.  Safety culture in the maternity unit of hospitals in Ilam province, Iran: a census survey using HSOPSC tool.

Authors:  Nahid Akbari; Marzieh Malek; Parvin Ebrahimi; Hamid Haghani; Sanaz Aazami
Journal:  Pan Afr Med J       Date:  2017-08-10

6.  A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study.

Authors:  Jordan McKenzie; Rasika Rajapakshe; Hua Shen; Shan Rajapakshe; Angela Lin
Journal:  JMIR Med Inform       Date:  2021-11-12
  6 in total

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