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A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results

Authors :
Kai J. Rogers
Matthew D. Krasowski
Source :
Data in Brief, Vol 47, Iss , Pp 109012- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Point-of-care testing is widely used in a variety of clinical settings. While this testing provides immediate and actionable clinical information, it is prone to error in both the interpretation and reporting of results. Point-of-care urinalysis presents unique opportunities for errors, ranging from variation in visual interpretation to input of results. The data included here represent the results from 63,279 urinalyses from 36,780 unique patients performed over a span of three years at an academic medical center and its associated clinics. The data include the patient age/legal sex, methodology (instrument and test strip used), and the available test results (color, clarity, glucose, bilirubin, ketones, specific gravity, blood, pH, protein, urobilinogen, nitrite, and leukocyte esterase). Additionally, we include the method of interface between the testing instrumentation and our electronic medical record (EMR). These fell into one of three broad categories: “Interfaced” (results directly transmitted from the urinalysis instrument to the EMR via specialized data interface), “Manual” (results input by selecting from a drop-down menu in the laboratory information system), and “Enter/Edit” (results typed freely into a text field in the EMR). Analysis of this data was primarily a direct comparison of detectable errors (typos, uninterpretable results, and results outside the reportable range) as a function of the method of entry into the EMR. Secondary analysis comparing the impact of restricting drop-down menu options for urine color and clarity was also performed. These data are of use to others as they are diverse in terms of the test performed and the method of interface. Others may wish to analyze these data when making decisions as to how to perform and report these tests and when estimating risks of error with various methods of data entry.

Details

Language :
English
ISSN :
23523409
Volume :
47
Issue :
109012-
Database :
Directory of Open Access Journals
Journal :
Data in Brief
Publication Type :
Academic Journal
Accession number :
edsdoj.6e58eaa2dace4bc18ada54d36e1b5267
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dib.2023.109012