Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that has gained significant relevance in the healthcare industry. NLP is a powerful tool that allows computers to process, interpret, and analyze large amounts of natural language data, which can be used to improve patient care and outcomes.
Applications of NLP in Healthcare
NLP is being used in several applications in the healthcare industry, including:
Electronic Health Records (EHR)
NLP can be used to make electronic health records more accessible and easier to understand. By using NLP, healthcare providers can extract relevant information from patient reports and results, which can be used to make more informed decisions.
A study published by the Journal of the American Medical Informatics Association found that NLP can improve the accuracy and speed of identifying patients with lung cancer by scanning EHRs.
Medical Coding and Billing
NLP is also being used to simplify medical coding and billing. Medical coding involves assigning specific codes to medical procedures and diagnoses to facilitate payment and insurance claims.
NLP can make this process faster and more accurate by automatically identifying and assigning the appropriate codes based on the patient's history and medical records.
Clinical Research
NLP can also be used in clinical research to identify potential patients for clinical trials. By scanning patient records and identifying individuals who meet certain criteria, NLP can help researchers find eligible participants for trials more quickly and efficiently.
A study published by the Journal of Biomedical Informatics found that NLP was able to identify eligible patients for clinical trials with 90% accuracy.
Challenges Faced by NLP in Healthcare
While NLP has immense potential in healthcare, there are still some challenges that need to be addressed before its full potential can be realized. Some of these challenges include:
Data Privacy and Security
NLP algorithms need access to large amounts of patient data to be effective. However, this data is often sensitive and must be protected to prevent data breaches and privacy violations.
Standardization of Terminology
Terminology used in healthcare can vary widely, even within the same institution. This can create challenges for NLP algorithms, which need to be trained on a specific set of terminology to be effective.
Limited Access to Data
Many healthcare providers are hesitant to share their patient data, which can limit the amount of data available to train NLP algorithms.
Conclusion
NLP has immense potential to revolutionize healthcare and improve patient outcomes. By automating manual processes and analyzing large amounts of patient data, NLP can help healthcare providers make more informed decisions and provide better care.
However, there are still some challenges that need to be addressed, including data privacy and security, standardization of terminology, and limited access to data. By addressing these challenges, the healthcare industry can fully realize the benefits of NLP.
References
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Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C., & Hurdle, J. F. (2008). Extracting information from textual documents in the electronic health record: A review of recent research. Yearbook of medical informatics, 128-144. doi: 10.1055/s-0038-1638621
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Li, X., Yang, Y., Zhang, X., & Lin, H. (2020). Incorporating Natural Language Processing into clinical decision support systems in oncology: Methods, applications, and challenges. Artifical Intelligence in Medicine, 104. doi: 10.1016/j.artmed.2020.101824
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Hanauer, D. A., & Zheng, K. (2016). Automated identification of adults with a high likelihood of undiagnosed HIV infection. Journal of the American Medical Informatics Association, 23(4), 764-768. doi: 10.1093/jamia/ocv160
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Sun, W., & Rumshisky, A. (2016). Automatic classification of sentences to support evidence based medicine. Journal of biomedical informatics, 63, 202-212. doi: 10.1016/j.jbi.2016.09.016
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Weng, C., Wu, X., Luo, Z., Boland, M. R., Theodoratos, D., Johnson, S. B., & Zheng, K. (2018). EliXR: An approach to eligibility criteria extraction and representation. Journal of biomedical informatics, 77, 129-138. doi: 10.1016/j.jbi.2017.12.014