CPT to RVU conversion improves model performance in the prediction of surgical case length

Abstract Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures i...

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Autores principales:	Nicholas Garside, Hamed Zaribafzadeh, Ricardo Henao, Royce Chung, Daniel Buckland
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f22351f315f647e690f6ee4cc7f80844
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spelling	oai:doaj.org-article:f22351f315f647e690f6ee4cc7f808442021-12-02T18:33:57ZCPT to RVU conversion improves model performance in the prediction of surgical case length10.1038/s41598-021-93573-22045-2322https://doaj.org/article/f22351f315f647e690f6ee4cc7f808442021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93573-2https://doaj.org/toc/2045-2322Abstract Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT–RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model’s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.Nicholas GarsideHamed ZaribafzadehRicardo HenaoRoyce ChungDaniel BucklandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicholas Garside Hamed Zaribafzadeh Ricardo Henao Royce Chung Daniel Buckland CPT to RVU conversion improves model performance in the prediction of surgical case length
description	Abstract Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT–RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model’s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.
format	article
author	Nicholas Garside Hamed Zaribafzadeh Ricardo Henao Royce Chung Daniel Buckland
author_facet	Nicholas Garside Hamed Zaribafzadeh Ricardo Henao Royce Chung Daniel Buckland
author_sort	Nicholas Garside
title	CPT to RVU conversion improves model performance in the prediction of surgical case length
title_short	CPT to RVU conversion improves model performance in the prediction of surgical case length
title_full	CPT to RVU conversion improves model performance in the prediction of surgical case length
title_fullStr	CPT to RVU conversion improves model performance in the prediction of surgical case length
title_full_unstemmed	CPT to RVU conversion improves model performance in the prediction of surgical case length
title_sort	cpt to rvu conversion improves model performance in the prediction of surgical case length
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f22351f315f647e690f6ee4cc7f80844
work_keys_str_mv	AT nicholasgarside cpttorvuconversionimprovesmodelperformanceinthepredictionofsurgicalcaselength AT hamedzaribafzadeh cpttorvuconversionimprovesmodelperformanceinthepredictionofsurgicalcaselength AT ricardohenao cpttorvuconversionimprovesmodelperformanceinthepredictionofsurgicalcaselength AT roycechung cpttorvuconversionimprovesmodelperformanceinthepredictionofsurgicalcaselength AT danielbuckland cpttorvuconversionimprovesmodelperformanceinthepredictionofsurgicalcaselength
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CPT to RVU conversion improves model performance in the prediction of surgical case length

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