Data-driven hospital personnel scheduling optimization through patients prediction

Feng，Defan1; Mo，Yu1; Tang，Zhiyao1; Chen，Quanjun1; Zhang，Haoran1; Akerkar，Rajendra2; Song，Xuan1

2021-03-01

10.1007/s42486-020-00052-0

CCF Transactions on Pervasive Computing and Interaction   影响因子和分区

2524-521X

2524-5228

With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China.

Hospital personnel scheduling Mixed-integer linear programming Time series prediction

ESCI ; EI

英语

第一 ; 通讯

Guangdong Provincial Key Laboratory[2020B121201001]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000710546200003

SPRINGERNATURE

20220811691795

Forecasting ; Hospitals ; Integer programming ; Personnel

Hospitals, Equipment and Supplies:462.2 ; Management:912.2 ; Personnel:912.4 ; Optimization Techniques:921.5

2-s2.0-85107916074

Scopus

1.SUSTech-UTokyo Joint Research Center on Super Smart City,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Western Norway Research Institute,Sogndal,Norway

Feng，Defan,Mo，Yu,Tang，Zhiyao,et al. Data-driven hospital personnel scheduling optimization through patients prediction[J]. CCF Transactions on Pervasive Computing and Interaction,2021,3(1):40-56.

Feng，Defan.,Mo，Yu.,Tang，Zhiyao.,Chen，Quanjun.,Zhang，Haoran.,...&Song，Xuan.(2021).Data-driven hospital personnel scheduling optimization through patients prediction.CCF Transactions on Pervasive Computing and Interaction,3(1),40-56.

Feng，Defan,et al."Data-driven hospital personnel scheduling optimization through patients prediction".CCF Transactions on Pervasive Computing and Interaction 3.1(2021):40-56.