We were unable to identify any publications that explore the utility of macros for sorting and formatting raw research data. They are a versatile tool in research and administrative settings for handling repetitive, tedious tasks in spreadsheet management and statistical analysis. Macros, short for ‘macroinstruction,’ are customizable software applications that transform inputs based on a pre-specified procedure or algorithm. We developed two Microsoft Excel (MS Excel) macros using Visual Basic for Applications (VBA) to facilitate the process of cleaning and compiling the vital sign and lab results exported from our EMR. We identified a method of exporting the necessary data from individual patient records using a clinician-facing function of our institution’s EMR, but the resulting datasets were disorganized and the process of methodically compiling this data using standard Excel functions would have also been time-consuming. The time required to individually transcribe each vital sign and lab value from the EMR to a spreadsheet for further analysis would have caused a significant delay in our study. The informatics service at our community-based teaching hospital helped us identify patients meeting our inclusion criteria but was unable to provide us with the requested retrospective data. Our team received Institutional Review Board (IRB) exemption for a chart review study that required vital sign and laboratory results from a set of prior hospitalizations. Therefore, chart review projects in these settings often rely on investigators manually abstracting data from individual patient records or having to clean large sets of raw clinical data, both of which are time-consuming processes. Researchers at community hospitals and smaller academic institutions are less likely to have assistance from informatics professionals who are trained in abstracting and cleaning clinical datasets retrieved from the electronic medical record (EMR) or a central data repository. Retrospective chart review research depends on obtaining clinical data in an efficient manner and in a format appropriate for statistical analysis. Results: Time spent on data cleaning was significantly reduced when using macro-assisted sorting compared to the manual approach for both vital signs (46.5 seconds versus 12.3 minutes per record, a 94% reduction P < 0.001) and labs (13.7 seconds versus 2.6 minutes per record, a 91% reduction P < 0.001).Ĭonclusions:Macros offer a flexible and efficient tool for cleaning large sets of clinical data, particularly when an institution lacks informatics support or EMR functionality to export clinical data in an analysis-ready format. The speed of macro-assisted data cleaning was compared to manual transcription. Two macros were developed to sort through these datasets and output them into a specified format. Methods: Using an intrinsic function of our institution’s EMR, vital signs and lab results from 20 individual hospitalizations were exported to a spreadsheet. Objectives: To demonstrate how macros may be useful for researchers at community hospitals and smaller academic health centers that lack informatics support. Macros are pre-programmed procedures that can be used in Microsoft Excel to help streamline the process of cleaning clinical datasets. Background: Retrospective chart review studies may be delayed by inability to export clean clinical data from an electronic medical record (EMR) or data repository.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |