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Automated identification of diagnostic labelling errors in medicine.

Authors :
Hautz, Wolf E.
Kündig, Moritz M.
Tschanz, Roger
Birrenbach, Tanja
Schuster, Alexander
Bürkle, Thomas
Hautz, Stefanie C.
Sauter, Thomas C.
Krummrey, Gert
Source :
Diagnosis (2194-802X). May2022, Vol. 9 Issue 2, p241-249. 9p.
Publication Year :
2022

Abstract

Keywords: decision support; diagnostic error; quality improvement EN decision support diagnostic error quality improvement 241 249 9 05/12/22 20220501 NES 220501 Introduction Diagnostic error is a frequent health care problem [[1]], [[2]], [[3]], [[4]] with major medical [[4]], [[5]], [[6]], legal [[7]], [[8]], [[9]] and economic consequences [[10]]. In the second dataset, 11 students only provided a diagnosis in five out of six cases, resulting in 109 out of 120 pairs of diagnoses being available for analysis. Three trained raters independently classified the pair of ER admittance diagnosis and internal medicine discharge diagnosis as either similar or discrepant, based on a previously validated scheme [[37]]. These are missed opportunities in diagnosis that did not result in patient harm, those that did (an entity he consequently terms "preventable diagnostic harm"), and delayed/wrong diagnosis without clear evidence of a missed opportunity, either with or without resulting harm [[18]]. [Extracted from the article]

Details

Language :
English
ISSN :
2194802X
Volume :
9
Issue :
2
Database :
Academic Search Index
Journal :
Diagnosis (2194-802X)
Publication Type :
Academic Journal
Accession number :
156787970
Full Text :
https://doi.org/10.1515/dx-2021-0039