Online Appointment Scheduling for a Nuclear Medicine Department in a Chinese Hospital

[Integrated care concepts and multidisciplinary process chains in a radiological context]

Modern patient-centered and cost-efficient care concepts in hospitals require the mapping of multidisciplinary process chains into clinical pathways. Clinical decision support systems and operations research methods use algorithms to classify patients into homogeneous groups and to model a complete clinical pathway for scheduling individual procedures. An improvement of the economic situation of the care facility can be achieved through improved resource utilization, reduced patient waiting times and a shortening of the length of stay. The interdisciplinary use of centrally stored interoperable information and comprehensive care management via information technology (IT) services lay the foundation for the dissolution of traditional IT system architectures in medicine and the development of flexibly integrable modern system platforms. New IT approaches such as the semantically standardized definition of procedures and resource properties, the use of clinical decision support systems and the use of service-oriented system architectures form the basis for the deep integration of radiology services into comprehensive interdisciplinary care concepts.

A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

Data-Driven Models for Capacity Allocation of Inpatient Beds in a Chinese Public Hospital

Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year's data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.

Analysis of the Impact of China’s Hierarchical Medical System and Online Appointment Diagnosis System on the Sustainable Development of Public Health: A Case Study of Shanghai

In the study of the sustainable development of public health in China, academic circles have little to do with the unique hierarchical medical system and online appointment diagnosis system in China’s medical system. Therefore, based on the medical situation in Shanghai, China, in addition to the traditional dimension of medical expenses, this paper fully considers the impact of the current hierarchical medical policy, constructs a selection model for medical treatment behavior under the hierarchical medical system and online appointment diagnosis system, and carries out simulation analysis through the cellular automata grid dynamic model. This paper finds that the time-cost-oriented medical treatment behavior of Chinese patients will have different distribution under the current hierarchical medical system and online appointment diagnosis system. (1) When the medical treatment system neither allows online appointment nor referral, a large number of patients congregated in high-grade hospitals, with the most unreasonable distribution. (2) With the implementation of the system of allowing referral and online appointment, patients are gradually diverted to lower-grade hospitals or off-peak hours, and the distribution is relatively improved. (3) If the medical treatment system allows both referral and online appointment, the distribution of patients is the most reasonable. Therefore, China’s current hierarchical medical system and online appointment diagnosis system will, to a considerable extent, become a policy tool that affects patients’ choice of hospitals and an effective means to achieve the rational allocation of existing medical resources, which will play an important role in the sustainable development of public health in China.

A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation.