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

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...
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Autor*in:	Kai J. Rogers [verfasserIn] Matthew D. Krasowski [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing

Übergeordnetes Werk:	In: Data in Brief - Elsevier, 2015, 47(2023), Seite 109012-
Übergeordnetes Werk:	volume:47 ; year:2023 ; pages:109012-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dib.2023.109012

Katalog-ID:	DOAJ087890046

Internformat


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520			\|a 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.
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allfields	10.1016/j.dib.2023.109012 doi (DE-627)DOAJ087890046 (DE-599)DOAJ6e58eaa2dace4bc18ada54d36e1b5267 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Kai J. Rogers verfasserin aut A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General) Matthew D. Krasowski verfasserin aut In Data in Brief Elsevier, 2015 47(2023), Seite 109012- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:47 year:2023 pages:109012- https://doi.org/10.1016/j.dib.2023.109012 kostenfrei https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340923001300 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 47 2023 109012-
spelling	10.1016/j.dib.2023.109012 doi (DE-627)DOAJ087890046 (DE-599)DOAJ6e58eaa2dace4bc18ada54d36e1b5267 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Kai J. Rogers verfasserin aut A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General) Matthew D. Krasowski verfasserin aut In Data in Brief Elsevier, 2015 47(2023), Seite 109012- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:47 year:2023 pages:109012- https://doi.org/10.1016/j.dib.2023.109012 kostenfrei https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340923001300 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 47 2023 109012-
allfields_unstemmed	10.1016/j.dib.2023.109012 doi (DE-627)DOAJ087890046 (DE-599)DOAJ6e58eaa2dace4bc18ada54d36e1b5267 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Kai J. Rogers verfasserin aut A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General) Matthew D. Krasowski verfasserin aut In Data in Brief Elsevier, 2015 47(2023), Seite 109012- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:47 year:2023 pages:109012- https://doi.org/10.1016/j.dib.2023.109012 kostenfrei https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340923001300 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 47 2023 109012-
allfieldsGer	10.1016/j.dib.2023.109012 doi (DE-627)DOAJ087890046 (DE-599)DOAJ6e58eaa2dace4bc18ada54d36e1b5267 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Kai J. Rogers verfasserin aut A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General) Matthew D. Krasowski verfasserin aut In Data in Brief Elsevier, 2015 47(2023), Seite 109012- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:47 year:2023 pages:109012- https://doi.org/10.1016/j.dib.2023.109012 kostenfrei https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340923001300 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 47 2023 109012-
allfieldsSound	10.1016/j.dib.2023.109012 doi (DE-627)DOAJ087890046 (DE-599)DOAJ6e58eaa2dace4bc18ada54d36e1b5267 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Kai J. Rogers verfasserin aut A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General) Matthew D. Krasowski verfasserin aut In Data in Brief Elsevier, 2015 47(2023), Seite 109012- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:47 year:2023 pages:109012- https://doi.org/10.1016/j.dib.2023.109012 kostenfrei https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340923001300 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 47 2023 109012-
language	English
source	In Data in Brief 47(2023), Seite 109012- volume:47 year:2023 pages:109012-
sourceStr	In Data in Brief 47(2023), Seite 109012- volume:47 year:2023 pages:109012-
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topic_facet	Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing Computer applications to medicine. Medical informatics Science (General)
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container_title	Data in Brief
authorswithroles_txt_mv	Kai J. Rogers @@aut@@ Matthew D. Krasowski @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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spellingShingle	Kai J. Rogers misc R858-859.7 misc Q1-390 misc Clinical laboratories misc Clinical laboratory information systems misc Clinical laboratory services misc Medical errors misc Point-of-care testing misc Computer applications to medicine. Medical informatics misc Science (General) A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
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topic_title	R858-859.7 Q1-390 A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results Clinical laboratories Clinical laboratory information systems Clinical laboratory services Medical errors Point-of-care testing
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title	A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
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title_full	A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
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title_sort	dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
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title_auth	A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results
url	https://doi.org/10.1016/j.dib.2023.109012 https://doaj.org/article/6e58eaa2dace4bc18ada54d36e1b5267 http://www.sciencedirect.com/science/article/pii/S2352340923001300 https://doaj.org/toc/2352-3409
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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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical laboratories</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical laboratory information systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical laboratory services</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical errors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Point-of-care testing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer applications to medicine. 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A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results

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