Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations? Article Swipe
Jun Kim
,
Andrew C. Vivas
,
Varun Arvind
,
Joseph V. Lombardi
,
Jay S. Reidler
,
Scott L. Zuckerman
,
Nathan J. Lee
,
Meghana Vulapalli
,
Eric Geng
,
Brian Cho
,
Kazuaki Morizane
,
Samuel K. Cho
,
Ronald A. Lehman
,
Lawrence G. Lenke
,
K. Daniel Riew
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1177/21925682211062831
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1177/21925682211062831
Study Design Retrospective Cohort Study. Objectives Using natural language processing (NLP) in combination with machine learning on standard operative notes may allow for efficient billing, maximization of collections, and minimization of coder error. This study was conducted as a pilot study to determine if a machine learning algorithm can accurately identify billing Current Procedural Terminology (CPT) codes on patient operative notes. Methods This was a retrospective analysis of operative notes from patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. Algorithm performance was measured by performing receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). A deep learning NLP algorithm and a Random Forest algorithm were both trained and tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model. Results The random forest machine learning model had an AUC of .94 and an AUPRC of .85. The deep learning model had a final AUC of .72 and an AUPRC of .44. The random forest model had a weighted average, class-by-class accuracy of 87%. The LSTM deep learning model had a weighted average, class-by-class accuracy 0f 59%. Conclusions Combining natural language processing with machine learning is a valid approach for automatic generation of CPT billing codes. The random forest machine learning model outperformed the LSTM deep learning model in this case. These models can be used by orthopedic or neurosurgery departments to allow for efficient billing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/21925682211062831
- https://journals.sagepub.com/doi/pdf/10.1177/21925682211062831
- OA Status
- gold
- Cited By
- 45
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214582408
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- OpenAlex ID
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https://openalex.org/W4214582408Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/21925682211062831Digital Object Identifier
- Title
-
Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-28Full publication date if available
- Authors
-
Jun Kim, Andrew C. Vivas, Varun Arvind, Joseph V. Lombardi, Jay S. Reidler, Scott L. Zuckerman, Nathan J. Lee, Meghana Vulapalli, Eric Geng, Brian Cho, Kazuaki Morizane, Samuel K. Cho, Ronald A. Lehman, Lawrence G. Lenke, K. Daniel RiewList of authors in order
- Landing page
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https://doi.org/10.1177/21925682211062831Publisher landing page
- PDF URL
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https://journals.sagepub.com/doi/pdf/10.1177/21925682211062831Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://journals.sagepub.com/doi/pdf/10.1177/21925682211062831Direct OA link when available
- Concepts
-
Artificial intelligence, Machine learning, Random forest, Receiver operating characteristic, Computer science, Current Procedural Terminology, Deep learning, Medicine, Natural language processing, Algorithm, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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45Total citation count in OpenAlex
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2025: 12, 2024: 21, 2023: 9, 2022: 3Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I98704320 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.98561759 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |