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数据分析与知识发现  2021, Vol. 5 Issue (9): 115-128     https://doi.org/10.11925/infotech.2096-3467.2020.1123
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
数据驱动的大型医院手术室运营预测与优化方法研究*
陈东华1,赵红梅2,尚小溥2,张润彤2()
1对外经济贸易大学信息学院 北京 100029
2北京交通大学经济管理学院 北京 100044
Optimizing Large Hospital Operating Rooms with Data Analytics
Chen Donghua1,Zhao Hongmei2,Shang Xiaopu2,Zhang Runtong2()
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
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摘要 

【目的】 研究相关性分析、关联规则挖掘和时间序列预测方法在手术室运营预测与优化的应用。【方法】 基于50 000余例手术记录数据,分别提出手术指标相关性分析、手术室资源关联规则挖掘和手术量时间序列预测方法,并据此探讨大型医院手术室运营预测与优化策略。【结果】 75%的手术操作时长与其他手术指标呈强线性相关性。FP-Growth算法在最小置信度0.85下能获得可靠的手术室资源使用规律。利用周手术量时间序列提高至少37.5%的预测精确度。【局限】 所用的手术室运营数据没有与其他医疗信息系统的数据关联,限制了该方法应用在医院其他部门的运营优化。同时,该方法在不同的医院运营环境中还需要进一步检验。【结论】 所提方法为实现数据驱动的大型医院手术室运营预测与优化目标提供方法论指导。

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陈东华
赵红梅
尚小溥
张润彤
关键词 手术室运营预测机器学习最优化方法    
Abstract

[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.

Key wordsOperating Room Operation Forecast    Machine Learning    Optimization Method
收稿日期: 2020-11-14      出版日期: 2021-06-29
ZTFLH:  分类号: TP319  
基金资助:*对外经济贸易大学中央高校基本科研业务费专项资金(20QD22);本文系对外经济贸易大学中央高校基本科研业务费专项资金(CXTD12-04);国家社会科学基金重大项目的研究成果之一(18ZDA086)
通讯作者: 张润彤     E-mail: rtzhang@bjtu.edu.cn
引用本文:   
陈东华,赵红梅,尚小溥,张润彤. 数据驱动的大型医院手术室运营预测与优化方法研究*[J]. 数据分析与知识发现, 2021, 5(9): 115-128.
Chen Donghua,Zhao Hongmei,Shang Xiaopu,Zhang Runtong. Optimizing Large Hospital Operating Rooms with Data Analytics. Data Analysis and Knowledge Discovery, 2021, 5(9): 115-128.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1123      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I9/115
Fig.1  研究思路
手术ID 年龄 身高 体重 dot dwdc dwa daop dat dws dl R2adj
OP05 0.937 3
OP19 0.935 8
OP09 0.912 8
OP14 0.912 1
OP16 0.900 2
OP13 0.898 3
OP02 0.875 8
OP18 0.859 1
OP04 0.843 2
OP10 0.775 5
OP15 0.740 2
OP01 0.729 3
OP12 0.689 6
OP17 0.544 6
OP06 0.528 8
OP03 0.244 9
OP07 0.219 5
OP08 0.076 5
OP20 0.074 9
OP11 0.019 4
Table 1  手术操作时间(dop)与其他时间指标变量之间的相关性分析
Fig.2  基于不同线性模型的OP01手术相关性分析R2值对比
手术ID LK F tr LK F te RB F tr RB F te PK F tr PK F te SK F tr SK F te
OP01 0.73 0.73 0.34 0.37 0.81* 0.73 -0.06 -0.12
OP02 0.68 0.85 0.25 -0.17 0.80 0.86* -0.01 -0.17
OP03 -2.21 0.53 0.34 0.44 0.76* 0.53 -0.07 -0.06
OP04 0.87 0.84 0.21 0.13 0.90* 0.89 -0.12 -0.29
OP05 0.95 0.98* 0.40 0.74 0.96 0.98 -0.23 -0.25
OP06 - - - - - - - -
OP07 -1.54 -2.27 0.07 0.04 0.89* 0.21 -0.02 -0.05
OP08 -1.84 -4.28 0.11 0.04 0.84* 0.04 -0.08 -0.09
OP09 0.88 0.92 0.29 0.21 0.91 0.93* -0.18 -0.14
OP10 0.80 0.78 0.20 0.16 0.80 0.81* -0.14 -0.06
OP11 -2.32 -12.00 0.13 0.18 0.85* 0.01 -0.12 -0.03
OP12 0.77* 0.72 0.13 0.06 0.77* 0.72 -0.12 -0.28
OP13 1.00 0.97* -0.02 0.16 1.00 0.97* -0.20 -0.08
OP14 0.87 0.92* -0.03 -0.18 0.87 0.92* -0.12 -0.20
OP15 0.67 0.76* 0.27 0.15 0.76* 0.76* -0.03 -0.07
OP16 0.89 0.90* 0.10 0.13 0.90* 0.90* -0.06 -0.01
OP17 0.73* 0.53 0.44 0.45 0.76 0.55 -0.05 -0.09
OP18 0.81 0.86* 0.32 0.37 0.81 0.86* -0.02 0.00
OP19 0.99* 0.95 0.34 0.53 0.99* 0.95 -0.09 -0.08
OP20 -3.19 -12.74 0.14 0.11 0.75* 0.39 -0.08 -0.19
Table 2  前20种手术建立SVR非线性回归模型的性能
Fig.3  聚类分析中不同手术的最优簇数比较
类别 置信度 关联规则
[病房,床号] →手术室 0.962 3 [病房06, 071] → 手术室01
0.946 4 [病房06, 073] → 手术室01
0.943 4 [病房06, 074] → 手术室01
0.943 4 [病房06, 070] → 手术室01
0.943 4 [病房06, 072] → 手术室01
0.913 8 [病房28, 032] → 手术室36
0.907 7 [病房17, 003] → 手术室26
0.888 9 [病房17, 010] → 手术室26
0.880 6 [病房17, 005] → 手术室26
[诊断,手术] →手术室 1.000 0 [下肢动脉硬化闭塞症, 股动脉球囊扩张成形术] → 手术室21
1.000 0 [肾病综合征, 经皮肾穿刺活组织检查] →手术室38
0.989 8 [ROP, 全麻检查] →手术室39
0.981 8 [ROP, 玻璃体腔注药术] → 手术室14
0.981 1 [鼻中隔偏曲, 鼻中隔偏曲矫正术] → 手术室22
0.980 4 [颈椎病, 颈椎前路手术] → 手术室08
0.973 0 [ROP, 全麻检查术] → 手术室39
0.969 2 [早孕, 电吸人流术] → 手术室37
0.944 4 [腺样体肥大, 鼻内镜下经鼻腺样体切除术] → 手术室22
0.941 9 [输卵管积水, 腹腔镜检查] → 手术室02
[病房]→手术室 1.000 0 [病房11] → 手术室16
0.994 8 [病房15] → 手术室16
0.980 6 [病房38] → 手术室38
0.966 3 [病房05] → 手术室06
0.938 9 [病房30] → 手术室30
0.908 5 [病房29] → 手术室21
0.893 9 [病房42] → 手术室40
0.882 7 [病房01] → 手术室30
0.866 0 [病房35] → 手术室04
Table 3  FP-Growth算法分析的结果汇总(最小支持度为50,最小置信度为0.85)
Fig.4  不同手术量时间序列预测模型的预测值与实际值对比分析
模型 序列 步数 FB MAE MSE RMSE AC C train AC C test
CNN TSday 5 -2.95 14.63 798.47 28.26 0.13 0.06
TSmonth 3 60.34 129.06 50 614.87 224.98 0.00 0.00
TSweek 31 -17.29 46.35 3 458.96 58.81 1.00* 0.11
CNN (交叉验证) TSday 8 -0.83 12.88 540.19 22.56 0.06 0.06
TSmonth - - - - - - -
TSweek 31 -4.42 41.67 2 908.20 52.03 1.00* 0.05
LSTM TSday 30 -2.77 13.05 556.72 23.59 0.06* 0.04
TSmonth 2 46.68 312.26 141 805.30 376.57 0.00 0.00
TSweek 18 141.75 172.29 38 600.82 196.47 0.00 0.00
MLP TSday 8 -0.77 13.82 597.89 24.45 0.21 0.06
TSmonth 2 2.73 280.36 107 555.10 327.96 1.00* 0.00
TSweek 22 -1.26 54.16 9 804.31 99.02 0.00 0.08
Table 4  基于不同手术时间序列的深度学习模型性能对比
[1] 杜金霞, 苗林平. 数据挖掘技术在医院信息处理中的应用研究[J]. 科学技术创新, 2020(22):106-107.
[1] ( Du Jinxia, Miao Linping. Application Research of Data Mining Technology in Medical Information Processing[J]. Scientific and Technological Innovation, 2020 (22):106-107.)
[2] 代伟, 徐勇勇, 曹秀堂, 等. 基于HIS环境的手术信息资源的数据挖掘与利用[J]. 医疗卫生装备, 2007, 28(3):35-36.
[2] ( 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.)
[3] 郭熙铜, 张晓飞, 刘笑笑, 等. 数据驱动的电子健康服务管理研究: 挑战与展望[J]. 管理科学, 2017, 30(1):3-14.
[3] ( 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.)
[4] 王春燕, 金燕, 吴燕燕. 手术室运营效率的影响因素与改进措施[J]. 护理实践与研究, 2018, 15(24):11-13.
[4] ( 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
[6] 陈睦, 许健, 黄宇飞, 等. 构建数据驱动医院科学决策的新模式[J]. 中国数字医学, 2015, 10(1):81-84.
[6] ( 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.)
[7] 张玉婷, 吾丽娜尔·吉格尔. 大数据驱动的公立医院公共卫生事业管理的机遇与挑战[J]. 世界最新医学信息文摘, 2019, 19(54):225-226.
[7] ( 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.)
[8] 谢金亮, 付敬, 罗婷婷, 等. 医院手术室运行效率分析及改进策略探讨[J]. 中国医院管理, 2014, 34(4):39-40.
[8] ( 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.)
[9] 关兵, 许海文, Amador J, 等. 精益医疗管理在手术室运营中的应用[J]. 解放军医院管理杂志, 2014, 21(11):1073-1076.
[9] ( 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.)
[10] 郭建新, 李炎, 李亚新, 等. 利用直线回归方程预测医院门急诊人次, 出院人数, 住院手术例数[J]. 医学理论与实践, 2014, 27(16):2236-2237.
[10] ( 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.)
[11] 高勇, 于芹, 余旭, 等. 手术室运行流程时间节点信息化管控平台[J]. 解放军医院管理杂志, 2020, 27(3):252-256.
[11] ( 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.)
[12] 王莉, 周娅颖, 程勤, 等. 手术室流程优化和时间管理效果评价[J]. 解放军医院管理杂志, 2017, 24(12):1123-1125.
[12] ( 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.)
[13] 孔珊珊, 申海艳, 伍沛, 等. 信息化技术在手术室管理中的应用进展[J]. 护理学杂志, 2019, 34(4):106-110.
[13] ( 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.)
[14] 牟冬梅, 杨鑫禹, 李茵, 等. 数据驱动医院管理决策的影响因素研究——基于扎根理论分析[J]. 现代情报, 2020, 40(8):89-97.
[14] ( 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.)
[15] 张润彤, 赵红梅, 许媛, 等. 数据驱动的大型医院门诊系统服务效率与患者流研究[J]. 管理科学, 2019, 32(6):72-85.
[15] ( 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
[17] 顾梅, 王雪梅, 施海彬. 基于ARIMA乘积季节模型的某医院介入导管室手术量预测研究[J]. 中国现代手术学杂志, 2020, 24(3):231-236.
[17] ( 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.)
[18] 徐一艺, 牛金海, 张坚. 聚类分析定制手术室医疗设备的个性化运维方案[J]. 生物医学工程学进展, 2019, 40(2):108-111.
[18] ( 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.
[24] 喻晓芬, 王峥, 过湘钗. 数据挖掘技术在手术室医院感染管理中的应用[J]. 中华医院感染学杂志, 2008, 18(1):78-81.
[24] ( 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.)
[25] 许多, 李影, 吴蒸. 基于数据挖掘分析的手术室病理标本质控优化研究[J]. 中国医院, 2016, 20(4):30-31.
[25] ( 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.)
[26] 白雪, 罗利, 李蓉梅. 医院管理中手术排程研究现状及发展前景[J]. 管理评论, 2011, 23(1):121-128.
[26] ( Bai Xue, Luo Li, Li Rongmei. Operating Room Scheduling: Research Overview and Prospect[J]. Management Review, 2011, 23(1):121-128.)
[27] 罗利. 医疗服务资源调度优化理论、方法及应用[M]. 北京: 科学出版社, 2014.
[27] ( Luo Li. Optimization in Healthcare Resource Planning and Scheduling Modeling: Theory, Methods and Applications[M]. Beijing: Science Press, 2014.)
[28] 王昱. 手术室能力分配与优化调度问题的随机规化和鲁棒优化方法研究[D]. 沈阳: 东北大学, 2015.
[28] ( 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
[31] 周晓叶. 数据挖掘技术在医院管理中的应用[J]. 中国医疗器械信息, 2017, 23(18):154-155.
[31] ( Zhou Xiaoye. Applications of Data Mining Technology in Hospital Management[J]. China Medical Device Information, 2017, 23(18):154-155.)
[32] 王莉, 曾俊, 程勤, 等. 利用医院现有资源提高手术间利用率[J]. 局解手术学杂志, 2012, 21(4):431.
[32] ( 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
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