The research was conducted using a large language model nicknamed NYUTron that was trained using millions of clinical notes, according to a June 7 news release. These notes were pulled from 336,000 patients who had received care within the NYU Langone hospital system in New York City between January 2011 and May 2020.
Unlike other technology that can only read data that’s been reformatted into standardized files, NYUTron can read any patient notes or records as written, even when a physician uses unique abbreviations.
NYUTron correctly identified 85 percent of those who died in the hospital and 79 percent of patients’ length of stay. The AI program was also able to identify the likelihood of additional conditions alongside a primary disease and the potential of an insurance denial.
At the Becker’s 32nd Annual Meeting: The Business and Operations of ASCs, taking place October 29-31 in Chicago, ASC leaders, surgeons and healthcare executives will explore strategies to drive growth, enhance operational performance, navigate reimbursement challenges and prepare for the future of ambulatory surgery. Apply for complimentary registration now.