Operating Room Usage Time Estimation with Machine Learning Models
Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/8/1518 |
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| author | Justin Chu Chung-Ho Hsieh Yi-Nuo Shih Chia-Chun Wu Anandakumar Singaravelan Lun-Ping Hung Jia-Lien Hsu |
| author_facet | Justin Chu Chung-Ho Hsieh Yi-Nuo Shih Chia-Chun Wu Anandakumar Singaravelan Lun-Ping Hung Jia-Lien Hsu |
| author_sort | Justin Chu |
| collection | DOAJ |
| description | Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability. |
| first_indexed | 2024-03-09T09:56:33Z |
| format | Article |
| id | doaj.art-a296b64687ce45cf984c26ae07c43a30 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T09:56:33Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-a296b64687ce45cf984c26ae07c43a302023-12-01T23:45:27ZengMDPI AGHealthcare2227-90322022-08-01108151810.3390/healthcare10081518Operating Room Usage Time Estimation with Machine Learning ModelsJustin Chu0Chung-Ho Hsieh1Yi-Nuo Shih2Chia-Chun Wu3Anandakumar Singaravelan4Lun-Ping Hung5Jia-Lien Hsu6Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanDepartment of General Surgery, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111045, TaiwanDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanGraduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanGraduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanNational Taipei University of Nursing and Health Sciences, Taipei City 112, TaiwanDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanEffectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability.https://www.mdpi.com/2227-9032/10/8/1518operating room usage timeschedulingmachine learningXGBoost |
| spellingShingle | Justin Chu Chung-Ho Hsieh Yi-Nuo Shih Chia-Chun Wu Anandakumar Singaravelan Lun-Ping Hung Jia-Lien Hsu Operating Room Usage Time Estimation with Machine Learning Models Healthcare operating room usage time scheduling machine learning XGBoost |
| title | Operating Room Usage Time Estimation with Machine Learning Models |
| title_full | Operating Room Usage Time Estimation with Machine Learning Models |
| title_fullStr | Operating Room Usage Time Estimation with Machine Learning Models |
| title_full_unstemmed | Operating Room Usage Time Estimation with Machine Learning Models |
| title_short | Operating Room Usage Time Estimation with Machine Learning Models |
| title_sort | operating room usage time estimation with machine learning models |
| topic | operating room usage time scheduling machine learning XGBoost |
| url | https://www.mdpi.com/2227-9032/10/8/1518 |
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