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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (9): 115-128    DOI: 10.11925/infotech.2096-3467.2020.1123
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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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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     
Received: 14 November 2020      Published: 29 June 2021
ZTFLH:  分类号: TP319  
Fund:*Fundamental Research Funds for the Central Universities in UIBE(20QD22);Fundamental Research Funds for the Central Universities in UIBE(CXTD12-04);National Social Science Fund of China(18ZDA086)
Corresponding Authors: Zhang Runtong     E-mail: rtzhang@bjtu.edu.cn

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1123     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I9/115

Research Framework
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
Correlation Analysis Between Surgical Procedure Duration and Other Time-Related Indicators
R2 Values Using Different Linear Models for Surgical Procedure OP01
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
Performance Comparison of Non-linear SVR Models Using Top 20 Surgical Procedures
Optimal Numbers of Clustering for Surgical Procedures in Cluster Analysis
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
Summary of Results Using FP-Growth Algorithm
Predicting and Expecting Values in Different Time Series Models of Numbers of Conducting Surgical Procedures
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
Performance Between Deep Learning Models for Different Time Series
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