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A method to identify pediatric high-risk diagnoses missed in the emergency department

A method to identify pediatric high-risk diagnoses missed in the emergency department AbstractBackground:Diagnostic error can lead to increased morbidity, mortality, healthcare utilization and cost. The 2015 National Academy of Medicine report “Improving Diagnosis in Healthcare” called for improving diagnostic accuracy by developing innovative electronic approaches to reduce medical errors, including missed or delayed diagnosis. The objective of this article was to develop a process to detect potential diagnostic discrepancy between pediatric emergency and inpatient discharge diagnosis using a computer-based tool facilitating expert review.Methods:Using a literature search and expert opinion, we identified 10 pediatric diagnoses with potential for serious consequences if missed or delayed. We then developed and applied a computerized tool to identify linked emergency department (ED) encounters and hospitalizations with these discharge diagnoses. The tool identified discordance between ED and hospital discharge diagnoses. Cases identified as discordant were manually reviewed by pediatric emergency medicine experts to confirm discordance.Results:Our computerized tool identified 55,233 ED encounters for hospitalized children over a 5-year period, of which 2161 (3.9%) had one of the 10 selected high-risk diagnoses. After expert record review, we identified 67 (3.1%) cases with discordance between ED and hospital discharge diagnoses. The most common discordant diagnoses were Kawasaki disease and pancreatitis.Conclusions:We successfully developed and applied a semi-automated process to screen a large volume of hospital encounters to identify discordant diagnoses for selected pediatric medical conditions. This process may be valuable for informing and improving ED diagnostic accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Diagnosis: Official Journal of the Society to Improve Diagnosis in Medicine (SIDM) de Gruyter

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Publisher
de Gruyter
Copyright
©2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2194-8011
eISSN
2194-802X
DOI
10.1515/dx-2018-0005
pmid
29858901
Publisher site
See Article on Publisher Site

Abstract

AbstractBackground:Diagnostic error can lead to increased morbidity, mortality, healthcare utilization and cost. The 2015 National Academy of Medicine report “Improving Diagnosis in Healthcare” called for improving diagnostic accuracy by developing innovative electronic approaches to reduce medical errors, including missed or delayed diagnosis. The objective of this article was to develop a process to detect potential diagnostic discrepancy between pediatric emergency and inpatient discharge diagnosis using a computer-based tool facilitating expert review.Methods:Using a literature search and expert opinion, we identified 10 pediatric diagnoses with potential for serious consequences if missed or delayed. We then developed and applied a computerized tool to identify linked emergency department (ED) encounters and hospitalizations with these discharge diagnoses. The tool identified discordance between ED and hospital discharge diagnoses. Cases identified as discordant were manually reviewed by pediatric emergency medicine experts to confirm discordance.Results:Our computerized tool identified 55,233 ED encounters for hospitalized children over a 5-year period, of which 2161 (3.9%) had one of the 10 selected high-risk diagnoses. After expert record review, we identified 67 (3.1%) cases with discordance between ED and hospital discharge diagnoses. The most common discordant diagnoses were Kawasaki disease and pancreatitis.Conclusions:We successfully developed and applied a semi-automated process to screen a large volume of hospital encounters to identify discordant diagnoses for selected pediatric medical conditions. This process may be valuable for informing and improving ED diagnostic accuracy.

Journal

Diagnosis: Official Journal of the Society to Improve Diagnosis in Medicine (SIDM)de Gruyter

Published: Jun 27, 2018

References