Improving Neurology Clinical Care With Natural Language Processing Tools
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Published on: July 2, 2013
Wendong Ge1, Hunter J Rice1, Irfan S Sheikh1
1From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA.
Abstract:
The integration of natural language processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security and substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.
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