Mount Sinai AI 'reads' physician notes to determine patient pain levels

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An artificial intelligence model developed by researchers from New York City-based Mount Sinai Health System is able to distinguish between acute and chronic lower back pain based on clinical notes in EHRs, a recent study finds.

Currently, while acute and chronic lower back pain are treated in different ways, they are coded the same way in the EHR. Proper treatment and billing can therefore only be determined by reviewing each patient's chart, including notes from their care team.

To make this process more efficient, the AI was trained on more than 17,400 clinical notes regarding 16,715 patients' back pain. As a result, the study's authors found that the AI was able to "read" physicians' notes and determine whether the pain was acute or chronic, which could potentially lead to improvements in coding, billing and treatment for the lower back pain.

"Several studies have documented increases in medication prescriptions and visits to physicians, physical therapists and chiropractors for lower back pain episodes," lead author Ismail Nabeel, MD, associate professor of environmental medicine and public health at Mount Sinai's Icahn School of Medicine, said in a release. "This study is important because artificial intelligence can potentially more accurately distinguish whether the pain is acute or chronic, which would determine whether a patient should return to normal activities quickly or rest and schedule follow-up visits with a physician."

Dr. Nabeel added, "This study also has implications for diagnosis, treatment and billing purposes in other musculoskeletal conditions, such as the knee, elbow and shoulder pain, where the medical codes also do not differentiate by pain level and acuity."

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