Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?

Kim, J. S., Vivas, A., Arvind, V., Lombardi, J., Reidler, J., Zuckerman, S. L., Lee, N. J., Vulapalli, M., Geng, E. A., Cho, B. H., Morizane, K., Cho, S. K., Lehman, R. A., Lenke, L. G., & Riew, K. D. (2022). Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations? Global Spine Journal, 13(7), 1946–1955. https://doi.org/10.1177/21925682211062831
Authors:
Jun S Kim
Andrew Vivas
Varun Arvind
Joseph Lombardi
Jay Reidler
Scott L Zuckerman
Nathan J Lee
Meghana Vulapalli
Eric A Geng
Brian H Cho
Kazuaki Morizane
Samuel K Cho
Ronald A Lehman
Lawrence G Lenke
Kiehyun Daniel Riew
Affiliated Authors:
Joseph Lombardi
Scott L Zuckerman
Nathan J Lee
Ronald A Lehman
Lawrence G Lenke
Columbia Affiliation:
Subjects:
Ethical Considerations in Clinical Photography and Documentation (OpenAlex Topic)
Development and Evaluation of Clinical Guidelines (OpenAlex Topic)
Impact of Health Information Technology in Healthcare (OpenAlex Topic)
Author Keywords:
machine learning
natural language processing
long short term
memory
random forest model learning
billing
surgery
current procedural terminology codes
coding
long short term, memory
elink
Publication Type:
Article
Unique ID:
10.1177/21925682211062831
PMID:
35225694
DOI:
10.1177/21925682211062831
Journal:
Global Spine Journal
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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View OpenAlex Record
View Web of Science Record

Record Created: