Literature DB >> 14728184

Critical gaps in the world's largest electronic medical record: Ad Hoc nursing narratives and invisible adverse drug events.

John F Hurdle1, Charlene R Weir, Beverly Roth, Jennifer Hoffman, Jonathan R Nebeker.   

Abstract

The Veterans Health Administration (VHA), of the U.S. Department of Veteran Affairs, operates one of the largest healthcare networks in the world. Its electronic medical record (EMR) is fully integrated into clinical practice, having evolved over several decades of design, testing, trial, and error. It is unarguably the world's largest EMR, and as such it makes an important case study for a host of timely informatics issues. The VHA consistently has been at the vanguard of patient safety, especially in its provider-oriented EMR. We describe here a study of a large set of adverse drug events (ADEs) that eluded a rigorous ADE survey based on prospective EMR chart review. These numerous ADEs were undetected (and hence invisible) in the EMR, missed by an otherwise sophisticated ADE detection scheme. We speculate how these invisible nursing ADE narratives persist and what they portend for safety re-engineering.

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Year:  2003        PMID: 14728184      PMCID: PMC1480185     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  15 in total

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Journal:  Pharm World Sci       Date:  1999-06

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Journal:  Am J Health Syst Pharm       Date:  1996-01-15       Impact factor: 2.637

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Journal:  JAMA       Date:  1995-07-05       Impact factor: 56.272

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Journal:  BMJ       Date:  1993-08-21

5.  Medication error reporting: a survey of nursing staff.

Authors:  J A Antonow; A B Smith; M P Silver
Journal:  J Nurs Care Qual       Date:  2000-10       Impact factor: 1.597

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Authors:  D W Bates; L L Leape; S Petrycki
Journal:  J Gen Intern Med       Date:  1993-06       Impact factor: 5.128

7.  Direct text entry in electronic progress notes. An evaluation of input errors.

Authors:  C R Weir; J F Hurdle; M A Felgar; J M Hoffman; B Roth; J R Nebeker
Journal:  Methods Inf Med       Date:  2003       Impact factor: 2.176

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Authors:  D C Classen; S L Pestotnik; R S Evans; J P Burke
Journal:  JAMA       Date:  1991-11-27       Impact factor: 56.272

9.  The incident reporting system does not detect adverse drug events: a problem for quality improvement.

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Journal:  Jt Comm J Qual Improv       Date:  1995-10

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  4 in total

1.  Managing the life cycle of electronic clinical documents.

Authors:  Thomas H Payne; Gail Graham
Journal:  J Am Med Inform Assoc       Date:  2006-04-18       Impact factor: 4.497

2.  Exploring the ability of natural language processing to extract data from nursing narratives.

Authors:  Sookyung Hyun; Stephen B Johnson; Suzanne Bakken
Journal:  Comput Inform Nurs       Date:  2009 Jul-Aug       Impact factor: 1.985

3.  Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning.

Authors:  Fei Li; Weisong Liu; Hong Yu
Journal:  JMIR Med Inform       Date:  2018-11-26

4.  Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.

Authors:  Tsendsuren Munkhdalai; Feifan Liu; Hong Yu
Journal:  JMIR Public Health Surveill       Date:  2018-04-25
  4 in total

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