Abstract 64: Chartsweep: A Hipaa-compliant Tool To Automate Chart Review For Plastic Surgery Research

Abstract

Purpose: Retrospective chart review (RCR) is the process of manual patient data review to answer research questions. Large study populations and heterogeneous data make this a tedious, biased and error-prone process1. The authors therefore designed and developed ChartSweep, a HIPAA-compliant Windows (Microsoft, WA, USA) application leveraging the Python coding language to streamline and expedite the RCR process while remaining faithful to its methodological rigor as outlined by Matt and Matthew. ChartSweep is open-source and can be customized for use with any electronic medical record system as part of any study requiring retrospective chart review. Methods: ChartSweep is a tool developed at the Massachusetts General Hospital. It uses the Selenium Python library to pull information from electronic medical records and securely store it in.csv,.txt,.pdf or.jpeg format. As a proof-of-concept, a retrospective review of 172 patient records stored in Epic (electronic medical record storage) was performed to identify subjects who had undergone radiofrequency ablation (RFA) of the greater or lesser occipital nerves (for treatment of migraine headache). The first search was conducted manually according to standard RCR methodology, the second automatically using ChartSweep. Automated ChartSweep output was then reviewed and patient charts describing RFA in other contexts (lumbar ablation, endometrial ablation) were manually excluded. Total time required for each review and discrepancies between data output were evaluated. Results: Overall manual review time was 1,371 minutes (23 hours) with a mean evaluation time per medical record of 8 minutes. Automated ChartSweep review was significantly faster requiring 56 minutes overall, and 0.3 minutes per patient record (P< 0.0001). Time saved was 7.6 minutes per chart and 1,315 minutes (21.9 hours) total. Both reviews identified 16 patients who had undergone RFA out of 172 total patients. Conclusion: Open-source Python libraries as leveraged by ChartSweep significantly accelerate the retrospective chart review process in plastic surgery research. Quality of data review is not compromised. Further analyses with larger study populations are required to validate ChartSweep as a reliable research tool. 1Matt, V., Matthew, H. J. J. o. e. e. f. h. p. The retrospective chart review: important methodological considerations. 2013;10.