Enhancing Clinical Efficiency: Utilizing ChatGPT for Streamlined Chart Review in Wound Care

Introduction: Traditional chart review in wound care research is increasingly challenged by a growing volume of patient data and extensive variables impacting wound outcomes. The emergence of Natural Language Processing (NLP) software such as ChatGPT presents a unique opportunity to automate the data extraction process. This study harnesses the capabilities of ChatGPT, hosted by our medical center’s secure, private Azure OpenAI service, to automatically extract and process variables from patient charts following sacral wound visits. We assess ChatGPT’s potential to revolutionize chart review through improved data retrieval accuracy and efficiency.We outline the methodology for implementing internal ChatGPT-based chart review and automated data post-processing, measure the reduction in time required for data extraction using the novel ChatGPT method, and validate the accuracy of ChatGPT-generated insights compared to manually reviewed charts.Methods:We evaluated the use of the medical center's internal ChatGPT in chart review. ChatGPT and a Python script were integrated into the existing chart review process for patients with sacral wounds from 2 hospital cohorts to extract and format variables related to wound care. Metrics include time taken for review and comparative accuracy of extracted information across and between providers.Results:ChatGPT achieved a 96.13% accuracy rate compared to manual chart review and identified an additional 1.40% of variables that were missed by manual reviewers. Furthermore, the average time per chart review decreased from 10.55 minutes with the manual method to 61.58 seconds using ChatGPT. ChatGPT was also able to synthesize accurate summaries based on patient history, including wound descriptions, prognoses, and risk assessments.  Discussion: The study highlights the potential of ChatGPT to enhance the speed and precision of chart review processes in the context of both clinical care and wound care research, offering valuable implications for the integration of artificial intelligence in healthcare workflows.References: