1School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China 2School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
[Objective] This study aims to optimize the management of large hospital operating rooms with the help of data analytics. [Objective] We collected about fifty thousand surgical cases from one large hospital in China. Then, we conducted regression analysis for correlation of surgical indicators, cluster analysis and association rule mining for resource usage, as well as the time series forecasting to predict the number of surgical cases. Finally, we discussed optimization strategies for operating rooms. [Results] The duration of 75% surgical procedures showed significant ties with other indicators. The FP-Growth algorithm with minimum confidence of 0.85 identified reliable patterns of resource usage. The accuracy of time series forecasting was increased by at least 37.5% due to the use of weekly numbers of surgical procedures. [Limitations] We did not link the operating room dataset with records from other medical information systems. Therefore, the proposed method might not work for other hospital departments. Meanwhile, our method needs to be examined with data from other hospitals. [Conclusions] This study could help to optimize the large hospital operating rooms.
( Du Jinxia, Miao Linping. Application Research of Data Mining Technology in Medical Information Processing[J]. Scientific and Technological Innovation, 2020 (22):106-107.)
( Dai Wei, Xu Yongyong, Cao Xiutang, et al. Data Mining and Utilization of Operation Information Sources of HIS[J]. Chinese Medical Equipment Journal, 2007, 28(3):35-36.)
( Guo Xitong, Zhang Xiaofei, Liu Xiaoxiao, et al. eHealth Service Management Research in the Big Data Era: Challenges and Future Directions[J]. Journal of Management Science, 2017, 30(1):3-14.)
( Wang Chunyan, Jin Yan, Wu Yanyan. Influencing Factors and Improvement Measures of Operating Efficiency in Operating Rooms[J]. Nursing Practice and Research, 2018, 15(24):11-13.)
[5]
May J H, Spangler W E, Strum D P, et al. The Surgical Scheduling Problem: Current Research and Future Opportunities[J]. Production and Operations Management, 2011, 20(3):392-405.
doi: 10.1111/poms.2011.20.issue-3
( Chen Mu, Xu Jian, Huang Yufei, et al. New Model of Building Data-driven Scientific Decision-Making of Hospital[J]. China Digital Medicine, 2015, 10(1):81-84.)
( Zhang Yuting, Wulinaer Jigeer. Opportunities and Challenges on Big Data Driven Public Hospital's Healthcare Management[J]. World Latest Medicine Information, 2019, 19(54):225-226.)
( Xie Jinliang, Fu Jing, Luo Tingting, et al. Efficiency Analysis and Its Improvement Strategy of Operation Room in a Hospital[J]. Chinese Hospital Management, 2014, 34(4):39-40.)
( Guan Bing, Xu Haiwen, Amador J, et al. Application of Refined Medical Care Management in Operation Room Process[J]. Hospital Administration Journal of Chinese PLA, 2014, 21(11):1073-1076.)
( Guo Jianxin, Li Yan, Li Yaxin, et al. Using the Linear Regression Equation to Predict the Number of Outpatient and Emergency Department Visits, the Number of Discharged Patients, and the Number of Inpatient Operations[J]. The Journal of Medical Theory and Practice, 2014, 27(16):2236-2237.)
( Gao Yong, Yu Qin, Yu Xu, et al. Establishing Informatization Control Platform Based on Operation Process Time Node of the Operating Room[J]. Hospital Administration Journal of Chinese PLA, 2020, 27(3):252-256.)
( Wang Li, Zhou Yaying, Cheng Qin, et al. Effect of Process Optimization and Time Management in Improving the Efficiency and Benefit of Operating Room[J]. Hospital Administration Journal of Chinese PLA, 2017, 24(12):1123-1125.)
( Kong Shanshan, Shen Haiyan, Wu Pei, et al. A Review of Information Technology in the Operating Room Management[J]. Journal of Nursing Science, 2019, 34(4):106-110.)
( Mu Dongmei, Yang Xinyu, Li Yin, et al. Research on Factors Influencing the Value of Data-Driven Hospital Management Decision——Based on Grounded Theory[J]. Journal of Modern Information, 2020, 40(8):89-97.)
( Zhang Runtong, Zhao Hongmei, Xu Yuan, et al. Relationship Between Service Efficiency and Patient Flow in Large Hospital Outpatient System: A Data Driven Perspective[J]. Journal of Management Science, 2019, 32(6):72-85.)
[16]
Luo L, Liu C, Feng L, et al. A Random Forest and Simulation Approach for Scheduling Operation Rooms: Elective Surgery Cancelation in a Chinese Hospital Urology Department[J]. The International Journal of Health Planning and Management, 2018, 33(4):941-966.
doi: 10.1002/hpm.2552
pmid: 29956373
( Gu Mei, Wang Xuemei, Shi Haibin. The Surgical Amount Prediction of the Interventional Catheterization Room in a Hospital Based on SA-RIMA Model[J]. Chinese Journal of Modern Operative Surgery, 2020, 24(3):231-236.)
( Xu Yiyi, Niu Jinhai, Zhang Jian. Cluster Analysis of Personalized Customized Maintenance Plans for Medical Equipment in Operating Rooms[J]. Progress in Biomedical Engineering, 2019, 40(2):108-111.)
[19]
Tiwari V, Furman W R, Sandberg W S. Predicting Case Volume from the Accumulating Elective Operating Room Schedule Facilitates Staffing Improvements[J]. Anesthesiology, 2014, 121(1):171-183.
doi: 10.1097/ALN.0000000000000287
[20]
Dexter F, Wachtel R E, Epstein R H. Event-Based Knowledge Elicitation of Operating Room Management Decision-Making Using Scenarios Adapted from Information Systems Data[J]. BMC Medical Informatics and Decision Making, 2011, 11:2.
doi: 10.1186/1472-6947-11-2
[21]
Eun J, Tiwari V, Sandberg W S. Predicting Daily Surgical Volumes Using Probabilistic Estimates of Providers' Future Availability[J]. Decision Sciences, 2020. DOI: 10.1111/deci.12478.
doi: 10.1111/deci.12478
[22]
Taaffe K, Pearce B, Ritchie G. Using Kernel Density Estimation to Model Surgical Procedure Duration[J]. International Transactions in Operational Research, 2021, 28(1):401-418.
doi: 10.1111/itor.v28.1
[23]
Stepaniak P S, Heij C, Mannaerts G H, et al. Modeling Procedure and Surgical Times for Current Procedural Terminology-anesthesia-surgeon Combinations and Evaluation in Terms of Case-duration Prediction and Operating Room Efficiency: A Multicenter Study[J]. Anesthesia & Analgesia, 2009, 109(4):1232-1245.
( Yu Xiaofen, Wang Zheng, Guo Xiangchai. Data Mining Technique and Its Application in Management Against Hospital Infection in Operating Room[J]. Chinese Journal of Nosocomiology, 2008, 18(1):78-81.)
( Xu Duo, Li Ying, Wu Zheng. Analysis on Improving the Management of Pathologic Specimen in Operating Room Based on Data Mining[J]. Chinese Hospitals, 2016, 20(4):30-31.)
( Wang Yu. Study on Stochastic Programming and Robust Optimization for Capacity Allocation and Scheduling Problems in Operating Rooms[D]. Shenyang: Northeastern University, 2015.)
[29]
王忠民. 医院集团模式下手术资源调度与协同优化研究[D]. 南京: 东南大学, 2018.
[29]
( Wang Zhongmin. Research on Surgical Operation Resource Scheduling and Collaborative Optimization in Hospital Group[D]. Nanjing: Southeast University, 2018.)
[30]
Krishna K, Murty N M. Genetic K-Means Algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999, 29(3):433-439.
doi: 10.1109/3477.764879
( Wang Li, Zeng Jun, Cheng Qin, et al. Optimizing the Utilization of Operating Room by Making Use of the Existing Resources in Hospitals[J]. Journal of Regional Anatomy and Operative Surgery, 2012, 21(4):431.)
[33]
Bodon F. A Fast APRIORI Implementation [C]//Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. 2003: 63.
[34]
Borgelt C. An Implementation of the FP-Growth Algorithm [C]//Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations. 2005: 1-5.
[35]
Pal S K, Mitra S. Multilayer Perceptron, Fuzzy Sets, and Classification[J]. IEEE Transactions on Neural Networks, 1992, 3(5):683-697.
pmid: 18276468
[36]
Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. Communications of the ACM, 2017, 60(6):84-90.
doi: 10.1145/3065386
[37]
Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.
pmid: 9377276
