Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China
Abstract
:1. Introduction
2. Related Works
2.1. Research on ERA
2.2. Research on the Intelligence of ERA
3. Allocation of Emergency Resources for Public Health Emergencies and Identification of Essential Intelligent Technologies
3.1. Characteristics of ERA for Public Health Emergencies
- (1)
- A multi-subject, multi-level super-network system. Firstly, multiple subjects are involved in ERA for public health emergencies. It requires the support and cooperation of multiple parties, such as the government, market and civil organizations, and the public. Second, because public health emergencies often involve a wide geographical area, the affected areas may be from the provinces, cities, villages, and towns. Therefore, from the government’s perspective, the resource allocation process cascades upward from lower government levels. The ERA system shows multi-level characteristics. At the same time, at least three levels of the deployment network exist from the supply point to the transit point and then to the demand point. The boundaries between supply, staging, and demand points are not apparent for significant public health emergencies. The point of supply for one resource may also be the point of demand for another. Physical networks, financial networks, and information networks are interwoven in the supply-and-demand networks to form a hyper-network system for ERA.
- (2)
- The disaster situation, resource needs, and priorities are dynamic. The initial transmission location of public health emergencies cannot be predicted. Different urban areas have differences in population density, economic level, road network, information communication, and other conditions. Therefore, there are differences in the emergency capacity and supply of emergency resources among different regions. The spread and destructiveness of viruses are not constant. Furthermore, medical resources are time-sensitive and often difficult to replace. Emergency resource needs and priorities change with the dynamics of an epidemic. It requires that each relief department be able to develop strategies and make timely allocation decisions in response to changes in the epidemic. Emergency resource allocation mechanisms must be well adapted to the dynamic variability of the epidemic, and they should also be able to make timely adjustments in response to dynamic demands in time and space.
- (3)
- The role of information resources. The rapid development of information technology has led to changes in the primary way of allocating emergency resources for public health emergencies. Mobile communications and the Internet have accelerated the speed of access and dissemination of information resources. However, due to the multiple sources of emergency information and non-uniform data formats, most demand-related information cannot be accessed timely. It makes the emergency information construction with information silos, information coupling, and poor information communication. Therefore, timely information response, fast transmission, and good analysis capabilities are the key issues to improving the efficiency of ERA. Building an emergency information management platform is a crucial way to improve emergency information management capability.
3.2. ERA Process for Public Health Emergencies
3.3. Essential Intelligent Technology Identification
4. Intelligent Emergency Resource Allocation Mechanism
5. Evaluation of Essential Intelligent Technologies for ERA
5.1. Indicator System Construction
5.2. Evaluation Model of Essential Intelligent Technologies for ERA Based on Entropy Value-TOPSIS Method
5.2.1. Entropy Power Method
5.2.2. Use TOPSIS Method to Identify Essential Intelligent Technologies
5.2.3. Experimental Results
6. Conclusions
6.1. Research Findings
- (1)
- ERA for public health emergencies is a multi-subject, multi-level super network-system, and the demand and priority of emergency resources change with the development of the epidemic. This paper categorizes emergency resources into material, human, information, and scientific and technological resources, and it focuses on the role of information resources. Information resources meet resource needs in ERA for public health emergencies while also playing the role of central control and auxiliary supervision. Intelligent emergency resource allocation mechanism gives full play to the characteristics of information resources. Based on establishing an information platform for ERA, this paper uses Intelligent Technology to make each allocation link intelligent, including medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence, in order to achieve efficient resource allocation and cost savings.
- (2)
- We conclude essential intelligent technologies through word frequency analysis of research on ERA for public health emergencies. Intelligent technologies include AI, the Internet, information systems, information technology, the Internet of Things, big data technology, etc. These intelligent technologies all play an essential role in developing intelligent ERA for public health emergencies.
- (3)
- This study establishes an evaluation index system for the essential intelligent technologies of ERA in four aspects: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. We used the entropy weight and TOPSIS methods to build the evaluation model for each intelligent technology. The results show that the evaluation indexes with greater weights are in medical intelligence and management intelligence. This indicates that medical intelligence and management intelligence are the focus of developing ERA intelligence. Furthermore, AI and big data technology have a significant key role in the ERA intelligence.
6.2. Key Research Insights
- (1)
- Pay attention to the role of information resources. Applying intelligent technology to acquiring, screening, storing, processing, and transmitting information resources in ERA is essential. This can help reduce the flow time of information resources and the response time of management departments, speed up the interaction rate, and ensure information security and timeliness. An information platform for ERA should be established promptly during the prevention and treatment of public health emergencies. All entities and levels should improve their information infrastructure and strengthen the application of big data, information systems, and information technology, forming a multi-level and regional information interconnection network crisscrossed vertically and horizontally. The management department should fully utilize intelligent devices for resource management and decision making. Intelligent decision making can enable all departments to respond quickly to changes in the epidemic.
- (2)
- Medical intelligence is the focus of ERA intelligence. Accelerating the process of intelligence in medical institutions and building intelligent buildings can help alleviate the phenomenon of medical resource tension, medical equipment congestion, and lack of medical personnel. Medical building intelligence is the introduction of Intelligent Technology and facilities and the improvement of internal organizational structure. The intelligence of medical buildings refers to the management style and the intelligence of management personnel. It is necessary to popularize the concept of competent healthcare, fully leverage the advantages of AI and logistics network technology, and improve the efficiency of medical resource utilization.
- (3)
- The resource management department should strengthen the application of information technology in intelligent resource management, and should use information systems to simplify management processes. “Machines replace humans” can reduce the labour-cost and block infection channels to the maximum extent. Departments related to decision making should use AI and expert systems to make ERA decisions, which can improve effectiveness and reduce the risks of decision making. The government should establish a specialized supervisory agency and use information technology to monitor the ERA process in real-time. This can ensure that resources are allocated and emergency resource needs are met, it can avoid problems such as information mismatch, allocation omission, or unfair allocation of resources.
6.3. Boundedness and Future Works
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No | Name of Intelligent Technology | Application Description of Intelligent Technology in Medical and Resource Allocation |
---|---|---|
1 | Artificial Intelligence | AI technologies can simulate, extend, and expand human intelligence to achieve functions, such as medical imaging-assisted diagnosis, intelligent drug development, intelligent health management, and assisted resource allocation decisions. |
2 | Internet | The Internet as a carrier and technical means can inform the process of ERA, realize instant communication, remote consultation, online consultation, etc. |
3 | (Medical) Information System | The Medical Information System can realize the storage, collection and query of information related to ERA. The information system assists emergency resource information management and conducts resource allocation planning. |
4 | Information Technology | The use of Information Technology can make the acquisition, transmission, processing, control, display, and storage of resource allocation information intelligent, such as radio frequency identification technology (RFID), sensing technology, network communication technology, etc. |
5 | The Internet of Things (IoT) | The Internet of Things (IoT) is to connect material resources with the network, exchange communication through information dissemination medium, and complete intelligent identification, positioning, tracking, supervision, and other functions. |
6 | Big Data | Big Data application technologies include data collection, data pre-processing, distributed storage, machine learning, etc. The deep combination of big data and cloud computing technology can realize functions such as epidemic prediction, intelligent medical care, resource management, and logistic network optimization. |
7 | Expert System | An Expert System is used to address problems in the field using the knowledge and the problem-solving methods of human experts, and it can assist in clinical medical diagnosis and ERA program decisions. |
8 | Intelligent Design | Intelligent Design is the application of modern information technology and computer simulation of human thinking activities, combined with neural networks and machine learning technology to assist in the automation of the design process. |
9 | Wearable Technology | Wearable technology is the embedding of multimedia, sensors, and wireless communication technologies into clothing and software support for data interaction and cloud interaction in order to help achieve real-time monitoring of the health status of a patient. |
10 | Machine Replacement Technology | Machine Replacement Technology is the use of robotic hands, automated control equipment, or assembly line automation for intelligent technology transformation of enterprises in order to achieve the purpose of reducing staff, increasing efficiency, improving quality, and ensuring safety. |
Primary Indicators | Secondary Indicators | Tertiary Indicators | Indicator Description | Indicator Attribute |
---|---|---|---|---|
Medical intelligence A1 | Medical resource savings B1 | Occupancy of medical devices C1 | The impact of intelligent technology on reducing medical device congestion | Positive |
Waiting time savings C2 | The impact of intelligent technology on reducing unnecessary waiting time for medical treatment | Positive | ||
Human resource savings C3 | The impact of intelligent technology on reducing the demand for medical personnel | Positive | ||
Use value B2 | Reaction time C4 | The reaction time consumed when intelligent technology provides interactive functions | Opposite | |
Consultation time C5 | The time spent on diagnosis and treatment using intelligent technology | Opposite | ||
Operational difficulty C6 | The operational difficulty of intelligent technology, i.e., the ability requirements for relevant operators | Opposite | ||
Cure rate C7 | The impact of intelligent technology on improving the cure rate | Positive | ||
Economic benefits B3 | Technology maturity C8 | The impact of intelligent technology on the accuracy and risk of medical diagnosis | Positive | |
Application breadth C9 | The application scope and popularity of intelligent technology in medical institutions | Positive | ||
Additional services C10 | The possibility of intelligent technology providing additional services | Positive | ||
Management intelligence A2 | Management cost savings B4 | Time-cost savings C11 | Quantitative indicators of time-cost savings in ERA using intelligent technology | Positive |
Manpower cost savings C12 | Quantitative indicators of manpower costs for ERA that can be saved by utilizing intelligent technology | Positive | ||
Management effectiveness B5 | Reduction of resource mismatch C13 | The impact of intelligent technology on reducing adverse phenomena such as resource mismatch and missed allocation | Positive | |
Managing information security C14 | The impact of using Intelligent Technology on resource management information security | Positive | ||
Decision-making intelligence A3 | Decision-making efficiency B6 | Information transmission speed C15 | The Impact of intelligent technology on the Transmission Speed of Decision Information and Decision Instructions | Positive |
Decision duration C16 | The reduction in decision-making time caused by intelligent technology | Positive | ||
Effects on decision-making B7 | Fault tolerance of decision-making C17 | The impact of intelligent technology on improving scheme fault tolerance and reducing decision risks | Positive | |
Effectiveness of decision-making C18 | The impact of intelligent technology on improving the effectiveness of decision-making plans | Positive | ||
Supervision intelligence A4 | Regulatory timeliness B8 | Alert response time C19 | The impact of intelligent technology on reducing the early warning response time of resource allocation regulation | Opposite |
Inspection time C20 | The impact of intelligent technology on reducing the inspection time of resource allocation supervision | Opposite | ||
Correction time C21 | The impact of intelligent technology on reducing error correction time for resource allocation supervision | Opposite | ||
Social benefit B9 | Social stability C22 | The impact of intelligent technology on improving social stability | Positive | |
International image C23 | The impact of intelligent technology on improving a country’s international image | Positive |
Index | Entropy | Weight (%) |
---|---|---|
C1 Occupancy of medical devices | 0.903 | 4.16 |
C2 Waiting time savings | 0.909 | 3.91 |
C3 Human resource savings | 0.907 | 3.99 |
C4 Reaction time | 0.926 | 3.20 |
C5 Consultation time | 0.903 | 4.17 |
C6 Operational difficulty | 0.912 | 3.77 |
C7 Cure rate | 0.814 | 8.00 |
C8 Technology maturity | 0.871 | 5.57 |
C9 Application breadth | 0.896 | 4.50 |
C10 Additional services | 0.920 | 3.44 |
C11 Time-cost savings | 0.932 | 2.94 |
C12 Manpower cost savings | 0.865 | 5.82 |
C13 Reduction of resource mismatch | 0.853 | 6.32 |
C14 Managing information security | 0.907 | 4.00 |
C15 Information transmission speed | 0.914 | 3.69 |
C16 Decision duration | 0.929 | 3.05 |
C17 Fault tolerance of decision-making | 0.903 | 4.19 |
C18 Effectiveness of decision-making | 0.869 | 5.62 |
C19 Alert response time | 0.917 | 3.57 |
C20 Inspection time | 0.926 | 3.19 |
C21 Correction time | 0.907 | 4.00 |
C22 Social stability | 0.865 | 5.82 |
C23 International image | 0.929 | 3.07 |
Index | Positive Ideal Solution | Negative Ideal Solution |
---|---|---|
C1 Occupancy of medical devices | 0.016 | 0.008 |
C2 Waiting time savings | 0.016 | 0.007 |
C3 Human resource savings | 0.017 | 0.010 |
C4 Reaction time | 0.012 | 0.008 |
C5 Consultation time | 0.016 | 0.011 |
C6 Operational difficulty | 0.014 | 0.009 |
C7 Cure rate | 0.031 | 0.020 |
C8 Technology maturity | 0.022 | 0.012 |
C9 Application breadth | 0.018 | 0.011 |
C10 Additional services | 0.013 | 0.008 |
C11 Time-cost savings | 0.011 | 0.007 |
C12 Manpower cost savings | 0.020 | 0.016 |
C13 Reduction of resource mismatch | 0.027 | 0.013 |
C14 Managing information security | 0.015 | 0.010 |
C15 Information transmission speed | 0.014 | 0.010 |
C16 Decision duration | 0.013 | 0.007 |
C17 Fault tolerance of decision-making | 0.018 | 0.008 |
C18 Effectiveness of decision-making | 0.024 | 0.012 |
C19 Alert response time | 0.014 | 0.008 |
C20 Inspection time | 0.013 | 0.007 |
C21 Correction time | 0.016 | 0.010 |
C22 Social stability | 0.022 | 0.014 |
C23 International image | 0.012 | 0.008 |
Intelligent Technology | Di+ | Di- | TOPSIS Closeness | Sort |
---|---|---|---|---|
AI | 0.009 | 0.030 | 0.766 | 1 |
Internet | 0.021 | 0.020 | 0.485 | 7 |
Information System | 0.020 | 0.022 | 0.518 | 4 |
Information Technology | 0.020 | 0.020 | 0.497 | 6 |
The Internet of Things | 0.020 | 0.021 | 0.513 | 5 |
Big Data | 0.013 | 0.027 | 0.683 | 2 |
Expert System | 0.020 | 0.022 | 0.529 | 3 |
Intelligent Design | 0.027 | 0.016 | 0.367 | 9 |
Wearable Technology | 0.029 | 0.011 | 0.276 | 10 |
Machine Replacement Technology | 0.026 | 0.017 | 0.405 | 8 |
Primary Indicators | Medical Intelligence A1 | Management Intelligence A2 | Decision-Making Intelligence A3 | Supervision Intelligence A4 | ||||
---|---|---|---|---|---|---|---|---|
Secondary Indicators | Medical resource savings B1 | 12.06 | Management cost savings B4 | 8.76 | Decision-making efficiency B6 | 6.74 | Regulatory timeliness B8 | 10.76 |
Use value B2 | 19.14 | Management effectiveness B5 | 10.32 | Effects on decision-making B7 | 9.81 | Social benefit B9 | 8.89 | |
Economic benefits B3 | 13.51 | |||||||
Weight | 44.71 | 19.08 | 16.55 | 19.65 | ||||
Average Weight | 4.47 | 4.77 | 4.13 | 3.93 |
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Ma, R.; Meng, F.; Du, H. Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China. Systems 2023, 11, 300. https://doi.org/10.3390/systems11060300
Ma R, Meng F, Du H. Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China. Systems. 2023; 11(6):300. https://doi.org/10.3390/systems11060300
Chicago/Turabian StyleMa, Ruhao, Fansheng Meng, and Haiwen Du. 2023. "Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China" Systems 11, no. 6: 300. https://doi.org/10.3390/systems11060300