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Motivated by a real problem in a big hospital in China, we study a daily surgery scheduling problem with operating room eligibility and dedicated surgeon. We model the problem as a parallel machine scheduling problem with machine and resource constraints to minimise the makespan, and innovatively propose an adaptive composite dispatching method to deal with such a strongly NP-hard problem. The dispatching rule is a combination of three popular rules LPT, LFJ and LRW, each of which can deal with some special features of the scheduling problems, and the scaling parameters are estimated through a statistical model learned from historical data. The adaptive composite dispatching method is easy-to-implement, fast, adaptable, robust and flexible. To examine the performance of the proposed solution approach, we first carry out a series of computational experiments showing that the adaptive composite dispatching method works very well compared to the optimal solution. Using a real data set, we further conduct a case study showing that our solution approach can improve the current practice by significantly shortening the makespan and reducing the overnight work. [ABSTRACT FROM AUTHOR]

The underutilization of primary care in urban China threatens the efficiency and effectiveness of the Chinese health system. To guide patient flow to primary care, the Chinese government has rolled out a sequence of health care reforms which improve the affordability, the infrastructure and workforce of the primary care system. However, these measures have not yielded the desired effect on the utilization of primary care, which is lowest in urban areas. It is unclear how the factors identified to influence facility choice in urban China are actually impacting choice behaviour. We conducted a discrete choice experiment to elicit the quantitative impact of facility attributes when choosing a health care facility for first visit and analysed how the stated choice varies with these attributes. We found that the respondents placed different weights on the identified attributes, depending on whether they perceived their condition to be minor or severe. For conditions perceived as minor, the respondents valued visit time, equipment and medical skill most. For conditions perceived as severe, they placed most importance on equipment, travel time and facility size. We found that for conditions perceived as minor, only 14% preferred visiting a facility over opting out, a percentage which would more than double to 37% if community health centres were maximally improved. For conditions perceived as severe, improvements in community health centres may almost double first visits to primary care, mostly from patients who would otherwise choose higher-level facilities. Our findings suggest that for both severity conditions, improvements to medical equipment and medical skill at community health centres in urban China can effectively direct patient flow to primary care and promote the efficiency and effectiveness of the urban health system. [ABSTRACT FROM AUTHOR]

Based on SO(3) rotation group theory, a method is presented to fit $$\alpha $$ $$\beta $$ -crystalline structure on to its corresponding electron microscopy (EM) model for the further diagnosis of crystalline disease. Taken EM model as a kind of $$\alpha $$ $$\beta $$ -crystalline model rotation deformation, image processing techniques are firstly preformed and then mesh generation adopted on the obtained EM, depending upon its molecular features. Secondly, rigid-body fitting is performed to fit $$\alpha $$ $$\beta $$ -crystalline Protein Data Bank (PDB) data onto its EM by using SO(3) rotation group theory. Among the fitting process, FFT is adopted to tackle the problems of large data and high computation. The PDB file is obtained from NCBI and EM file obtained from EMBL-EBI. All of the tools are developed by CVC, ICES, the University of Texas at Austin. The experimental results show that it's a precise and efficient method of fitting $$\alpha $$ $$\beta $$ -crystalline PDB crystal structure onto its EM model. [ABSTRACT FROM AUTHOR]