MedPromptExtract (Medical Data Extraction Tool): Anonymization and High-Fidelity Automated Data Extraction Using...

The labor-intensive nature of data extraction from sources like discharge summaries (DSs) poses significant obstacles to the digitization of medical records particularly for low- and middle-income countries (LMICs). In this paper we present a completely automated method, MedPromptExtract, to efficiently extract data from DS while maintaining confidentiality.

The source of data were DSs from Kokilaben Dhirubhai Ambani Hospital (KDAH) of patients having acute kidney injury (AKI). A pre-existing tool, Expert-Informed Joint Learning aGgrEatioN (EIGEN), which leverages semi-supervised learning techniques for high-fidelity information extraction, was used to anonymize the DSs, and natural language processing (NLP) was used to extract data from regular fields. We used prompt engineering and a large language model (LLM) to extract custom clinical information from free-flowing text describing the patient's stay in the hospital. Twelve features associated with the occurrence of AKI were extracted. The LLM's responses were validated against clinicians' annotations.

The MedPromptExtract tool first subjected DSs to the anonymization pipeline, which took 3 seconds per summary. Successful anonymization was verified by clinicians, thereafter the NLP pipeline extracted structured text from the anonymized pdfs at the rate of 0.2 s per summary with 100% accuracy. Finally, DSs were analysed by the LLM pipeline using Gemini Pro for the 12 features. Accuracy metrics were calculated by comparing model responses to clinicians' annotations with 7 features achieving Area Under the Curve (AUC) above 0.9, indicating the high fidelity of the extraction process.

MedPromptExtract serves as an automated adaptable tool for efficient data extraction from medical records with a dynamic user interface.