Get out what you put in: optimising electronic medical record data

Brandon Stretton; Joshua Kovoor; Aashray Gupta; Lewis Hains; Stephen Bacchi; Bianca Wong; Patrick G. O'Callaghan; Savio Barreto; Thomas J. Hugh; Elizabeth Murphy; Markus Trochsler; Robert Padbury; Mark Boyd; Guy Maddern

doi:10.1111/ans.18559

Get out what you put in: optimising electronic medical record data

Brandon Stretton, Joshua Kovoor, Aashray Gupta, Lewis Hains, Stephen Bacchi, Bianca Wong, Patrick G. O'Callaghan, Savio Barreto, Thomas J. Hugh, Elizabeth Murphy, Markus Trochsler, Robert Padbury, Mark Boyd, Guy Maddern

College of Medicine and Public Health

Research output: Contribution to journal › Comment/debate

2 Citations (Scopus)

58 Downloads (Pure)

Abstract

As clinicians and researchers, it is our responsibility to use the data available to us to optimise the care of our patients. These data are vast and should, in theory, be able to provide granular information to make appropriate diagnostic and management decisions to provide the best care for patients. However, these data can take many forms in electronic medical records (EMR) that are also not designed to output the information in a coordinated manner. Accordingly, the measurement of data in EMR is often tenuous and slow. Now, with many institutions transitioning from handwritten to EMR software, foresight and a coordinated approach is required to transition to the digital age.

Original language	English
Pages (from-to)	2056-2058
Number of pages	3
Journal	ANZ Journal of Surgery
Volume	93
Issue number	9
DOIs	https://doi.org/10.1111/ans.18559
Publication status	Published - Sept 2023

Keywords

Electronic medical records
medical data
research activities
audit activities

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10.1111/ans.18559Licence: CC BY

Final published versionFinal published version, 291 KBLicence: CC BY

Cite this

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title = "Get out what you put in: optimising electronic medical record data",

abstract = "As clinicians and researchers, it is our responsibility to use the data available to us to optimise the care of our patients. These data are vast and should, in theory, be able to provide granular information to make appropriate diagnostic and management decisions to provide the best care for patients. However, these data can take many forms in electronic medical records (EMR) that are also not designed to output the information in a coordinated manner. Accordingly, the measurement of data in EMR is often tenuous and slow. Now, with many institutions transitioning from handwritten to EMR software, foresight and a coordinated approach is required to transition to the digital age.",

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T2 - optimising electronic medical record data

AU - Stretton, Brandon

AU - Kovoor, Joshua

AU - Gupta, Aashray

AU - Hains, Lewis

AU - Bacchi, Stephen

AU - Wong, Bianca

AU - O'Callaghan, Patrick G.

AU - Barreto, Savio

AU - Hugh, Thomas J.

AU - Murphy, Elizabeth

AU - Trochsler, Markus

AU - Padbury, Robert

AU - Boyd, Mark

AU - Maddern, Guy

PY - 2023/9

Y1 - 2023/9

N2 - As clinicians and researchers, it is our responsibility to use the data available to us to optimise the care of our patients. These data are vast and should, in theory, be able to provide granular information to make appropriate diagnostic and management decisions to provide the best care for patients. However, these data can take many forms in electronic medical records (EMR) that are also not designed to output the information in a coordinated manner. Accordingly, the measurement of data in EMR is often tenuous and slow. Now, with many institutions transitioning from handwritten to EMR software, foresight and a coordinated approach is required to transition to the digital age.

AB - As clinicians and researchers, it is our responsibility to use the data available to us to optimise the care of our patients. These data are vast and should, in theory, be able to provide granular information to make appropriate diagnostic and management decisions to provide the best care for patients. However, these data can take many forms in electronic medical records (EMR) that are also not designed to output the information in a coordinated manner. Accordingly, the measurement of data in EMR is often tenuous and slow. Now, with many institutions transitioning from handwritten to EMR software, foresight and a coordinated approach is required to transition to the digital age.

KW - Electronic medical records

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KW - research activities

KW - audit activities

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DO - 10.1111/ans.18559

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JO - ANZ Journal of Surgery

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Get out what you put in: optimising electronic medical record data

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Keywords

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