@inproceedings{krishnamoorthy-singh-2023-taxonomy,
title = "Taxonomy-Based Automation of Prior Approval Using Clinical Guidelines",
author = "Krishnamoorthy, Saranya and
Singh, Ayush",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.65",
pages = "598--607",
abstract = "Performing prior authorization on patients in a medical facility is a time-consuming and challenging task for insurance companies. Automating the clinical decisions that lead to authorization can reduce the time that staff spend executing such procedures. To better facilitate such critical decision making, we present an automated approach to predict one of the challenging tasks in the process called primary clinical indicator prediction, which is the outcome of this procedure. The proposed solution is to create a taxonomy to capture the main categories in primary clinical indicators. Our approach involves an important step of selecting what is known as the {``}primary indicator{''} {--} one of the several heuristics based on clinical guidelines that are published and publicly available. A taxonomy based PI classification system was created to help in the recognition of PIs from free text in electronic health records (EHRs). This taxonomy includes comprehensive explanations of each PI, as well as examples of free text that could be used to detect each PI. The major contribution of this work is to introduce a taxonomy created by three professional nurses with many years of experience. We experiment with several state-of-the-art supervised and unsupervised techniques with a focus on prior approval for spinal imaging. The results indicate that the proposed taxonomy is capable of increasing the performance of unsupervised approaches by up to 10 F1 points. Further, in the supervised setting, we achieve an F1 score of 0.61 using a conventional technique based on term frequency{--}inverse document frequency that outperforms other deep-learning approaches.",
}
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<abstract>Performing prior authorization on patients in a medical facility is a time-consuming and challenging task for insurance companies. Automating the clinical decisions that lead to authorization can reduce the time that staff spend executing such procedures. To better facilitate such critical decision making, we present an automated approach to predict one of the challenging tasks in the process called primary clinical indicator prediction, which is the outcome of this procedure. The proposed solution is to create a taxonomy to capture the main categories in primary clinical indicators. Our approach involves an important step of selecting what is known as the “primary indicator” – one of the several heuristics based on clinical guidelines that are published and publicly available. A taxonomy based PI classification system was created to help in the recognition of PIs from free text in electronic health records (EHRs). This taxonomy includes comprehensive explanations of each PI, as well as examples of free text that could be used to detect each PI. The major contribution of this work is to introduce a taxonomy created by three professional nurses with many years of experience. We experiment with several state-of-the-art supervised and unsupervised techniques with a focus on prior approval for spinal imaging. The results indicate that the proposed taxonomy is capable of increasing the performance of unsupervised approaches by up to 10 F1 points. Further, in the supervised setting, we achieve an F1 score of 0.61 using a conventional technique based on term frequency–inverse document frequency that outperforms other deep-learning approaches.</abstract>
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%0 Conference Proceedings
%T Taxonomy-Based Automation of Prior Approval Using Clinical Guidelines
%A Krishnamoorthy, Saranya
%A Singh, Ayush
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F krishnamoorthy-singh-2023-taxonomy
%X Performing prior authorization on patients in a medical facility is a time-consuming and challenging task for insurance companies. Automating the clinical decisions that lead to authorization can reduce the time that staff spend executing such procedures. To better facilitate such critical decision making, we present an automated approach to predict one of the challenging tasks in the process called primary clinical indicator prediction, which is the outcome of this procedure. The proposed solution is to create a taxonomy to capture the main categories in primary clinical indicators. Our approach involves an important step of selecting what is known as the “primary indicator” – one of the several heuristics based on clinical guidelines that are published and publicly available. A taxonomy based PI classification system was created to help in the recognition of PIs from free text in electronic health records (EHRs). This taxonomy includes comprehensive explanations of each PI, as well as examples of free text that could be used to detect each PI. The major contribution of this work is to introduce a taxonomy created by three professional nurses with many years of experience. We experiment with several state-of-the-art supervised and unsupervised techniques with a focus on prior approval for spinal imaging. The results indicate that the proposed taxonomy is capable of increasing the performance of unsupervised approaches by up to 10 F1 points. Further, in the supervised setting, we achieve an F1 score of 0.61 using a conventional technique based on term frequency–inverse document frequency that outperforms other deep-learning approaches.
%U https://aclanthology.org/2023.ranlp-1.65
%P 598-607
Markdown (Informal)
[Taxonomy-Based Automation of Prior Approval Using Clinical Guidelines](https://aclanthology.org/2023.ranlp-1.65) (Krishnamoorthy & Singh, RANLP 2023)
ACL