AI reads doctors' notes at scale, revealing data absent from coded medical records
Every time a person sees a doctor, the visit produces two kinds of records. There are the tidy checkbox fields: the billing codes, the lab values, the prescription entries. And there is the note the clinician actually writes, describing what the patient said, how they are coping, what side effects they mentioned and why a medication was changed.
Medical notes contain valuable insights that are often overlooked in coded medical records. A new study published in Nature Medicine demonstrates a system capable of accurately reading these notes at scale, extracting data, and linking it back to the original text. The researchers found that while the system scored 99.4% accuracy, it still had variability in its findings.
The study applied this system to over 16,000 adults using GLP-1 receptor agonist medications for diabetes or weight loss. The results showed that patients with better initial blood sugar control lost more weight faster, and those with more severe depression showed the most improvement. These observations were only possible due to the system's ability to analyze the narrative text in medical notes, which contains a significant amount of valuable data not captured in coded records.
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