Treffer: Researchers at New York University (NYU) Langone Health Report New Data on Gastrointestinal Endoscopy (Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data:...).

The article focuses on a study conducted by researchers at New York University (NYU) Langone Health, which developed an automated workflow using machine learning (ML) and robotic process automation (RPA) to improve the documentation of colorectal cancer (CRC) screening follow-up dates from colonoscopy reports. The study aimed to address the challenges of extracting data from unstructured colonoscopy reports and integrating it into electronic health records (EHR). The proof-of-concept process demonstrated an accuracy of 80.7% in identifying valid follow-up dates and processed over 16,500 reports, indicating the potential for enhanced patient recall and reduced documentation burden. The research highlights the feasibility of using automated workflows to tackle interoperability issues within healthcare systems. [Extracted from the article]