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Interactive Analysis of a Web-Based Quality Assurance System Using Python

D Dunkerley*, D Jacqmin , University of Wisconsin- Madison, Middleton, WI


(Saturday, 3/30/2019) 10:30 AM - 12:30 PM

Room: Osceola Ballroom C

Purpose: Incident learning systems in radiation oncology are an important quality and safety tool, and studies have shown that high participation in incident reporting is associated with good safety culture and quality care. However, evaluating, managing and organizing a large incident reporting system is a time- and labor-intensive process. The purpose of this work is to develop a graphical user interface (GUI) to facilitate the processing and review of in-house web-based quality assurance forms.

Methods: Python was selected as the programming language due to its flexibility and wide availability of libraries in addition to its ease of use for future adaptation. The GUI permits selecting and sorting the data according to different QA form data elements and custom date ranges. The GUI also includes a form browser and tools for cross-correlational and longitudinal analysis.

Results: Reading data forms using the program is completely automated, takes 0.9 seconds per form, and removes the potential for transcription errors. The current database of 745 QA forms loads in approximately 10 minutes, and thereafter analysis is instantaneous. The previous data aggregation method (manually analysis) took hours and was limited in the types of analysis that could be done due to the time-consuming nature of the work.

Conclusion: In contrast to reviewing QA forms individually, data aggregation and processing provides a broad overview to view trends. The use of a GUI allows those without programming experience to easily review data. The GUI developed in this work provides the ability for those interested in quality assurance to quickly and effectively analyze aggregated data for correlation and trends. The intuitive interface makes it easy to identify issues that happen at a higher frequency, which allows our clinic to direct quality improvement efforts to where they may have the largest impact.

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