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Introduction/Purpose: Ultrasound picture archiving and communication system (PACS) databases are useful for quality improvement and clinical research but frequently contain free text that is not easily readable. Here, we present a method to extract and clean a semi‐structured echocardiography (cardiac ultrasound) PACS database. Methods: Echocardiography studies between 1 January 2010 and 31 December 2018 were extracted using a data mining tool. Numeric variables were recoded with extreme values excluded. Analysis of free text, including descriptions of the heart valves and right and left ventricular size and function, was performed using a rule‐based system. Different levels of free text variables were initially identified using commonly used phrases and then iteratively developed. Randomly selected sets of 100 studies were compared to the electronic health record to validate the data cleaning process. Results: The data validation step was performed three times in total, with Cohen's kappa ranging between 0.88 and 1.00 for the final set of data validation across all measures. Conclusion: Free text cleaning of semi‐structured PACS databases is possible using freely available open‐source software. The accuracy of this method is high, and the resulting dataset can be linked to administrative data to answer research questions. We present a method that could be used to answer clinical questions or to develop quality improvement initiatives. [ABSTRACT FROM AUTHOR]

Background: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures., Objectives: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures., Methods: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided., Results: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future., Conclusions: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research., (Copyright © 2024 Australian College of Critical Care Nurses Ltd. All rights reserved.)