Hospital medical equipment management method based on Internet of Things and AI

Abstract

The invention discloses a hospital medical equipment management method based on Internet of Things and AI, and aims to optimize the use efficiency and management level of medical equipment. The system comprises an equipment sensing layer, a data transmission layer, a data processing layer and an application service layer, equipment data are collected in real time through an intelligent sensor and an RFID tag, efficient data transmission is achieved through 5G communication and edge calculation, data analysis is conducted through deep learning, machine learning and reinforcement learning, and the data transmission efficiency is improved. And finally, a visual management interface is provided, and the functions of real-time monitoring, statistical analysis, maintenance reminding, energy consumption monitoring and the like of the equipment are supported. The system is suitable for managing a monitor, an infusion pump, an electrocardiograph, an ultrasonic instrument, an X-ray device, an MRI (Magnetic Resonance Imaging) machine and other various medical devices, and has the remarkable benefits of improving the utilization rate of the devices, reducing the maintenance cost, reducing the downtime and the like. According to the invention, an innovative solution is provided for full-life-cycle management of hospital medical equipment.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment management technology, and in particular to a hospital medical equipment management method based on the Internet of Things and AI, which is suitable for optimizing the utilization efficiency and management level of medical equipment. Background Art

[0002] With the rapid advancement of medical technology, the variety and quantity of medical equipment used in modern hospitals has increased significantly, including monitors, infusion pumps, electrocardiograms, ultrasound machines, X-ray equipment, and MRI machines. These devices play a vital role in diagnosis, treatment, and patient monitoring. However, traditional medical equipment management approaches face numerous challenges, severely impacting hospital operational efficiency and service quality. In many hospitals, medical equipment utilization is generally inefficient, with high equipment idle rates, irrational allocation, and duplicate purchases leading to resource waste. For example, certain equipment may be used less frequently in some departments while in short supply in others. This uneven resource allocation not only increases hospital operating costs but can also impact patient experience. Medical equipment maintenance is a crucial component of hospital management. Traditional management approaches rely on manual inspections and scheduled maintenance, making it difficult to monitor equipment operating status in real time, leading to untimely or excessive maintenance. Statistics show that downtime due to equipment failures accounts for 10%-15% of total equipment usage, increasing repair costs and potentially delaying patient treatment.

[0003] Traditional medical device management lacks data-driven decision support, making it difficult to achieve refined management. For example, equipment procurement decisions are often based on experience rather than actual needs, leading to resource waste or equipment shortages. Furthermore, information such as equipment usage data, fault records, and maintenance history is scattered across disparate systems, making comprehensive analysis difficult and limiting management efficiency. Medical device safety and compliance are crucial aspects of hospital management. Traditional management methods struggle to monitor equipment operating status in real time, hindering the timely identification of potential safety hazards. Furthermore, equipment maintenance records and calibration information may be incomplete, increasing the risk of medical malpractice. The rapid development of the Internet of Things (IoT) and artificial intelligence (AI) technologies in recent years has provided new solutions to these challenges. IoT technology uses sensors and RFID tags to enable real-time monitoring and data collection of equipment, providing a data foundation for equipment management. AI technology, through deep learning and machine learning algorithms, analyzes this data to enable functions such as equipment usage prediction, fault warning, and optimized scheduling. While IoT and AI technologies demonstrate significant potential for medical device management, existing systems still have limitations, such as data silos, insufficient algorithm accuracy, and difficulties in system integration. Equipment data is scattered across different systems, making data sharing and comprehensive analysis difficult. Existing prediction models and scheduling algorithms have limited accuracy and are unable to meet the actual needs of hospitals. Existing systems are often difficult to seamlessly integrate with other hospital information systems (such as HIS, LIS, and PACS), limiting the scope of application of the system.

[0004] In response to the above problems, the present invention proposes a hospital medical equipment management method based on the Internet of Things and artificial intelligence. By integrating advanced IoT and AI technologies, it realizes the full life cycle management of medical equipment, optimizes the efficiency of equipment use, reduces operating costs, and improves the overall management level of the hospital. The system can not only monitor the status and usage of the equipment in real time, but also provide intelligent decision-making support through data analysis, providing innovative solutions for hospital medical equipment management. In summary, traditional medical equipment management methods can no longer meet the needs of modern hospitals, and the application of Internet of Things and artificial intelligence technologies provides new possibilities for solving these problems. The present invention is intended to fill the gaps in the existing technology and provide efficient, intelligent and reliable solutions for hospital medical equipment management. Summary of the Invention

[0005] To address the technical issues outlined above, this paper develops a hospital medical equipment management method based on the Internet of Things and AI. This approach aims to address the challenges inherent in traditional medical equipment management, such as low equipment utilization efficiency, high maintenance costs, prolonged downtime, and a lack of data support for management decisions. By integrating advanced IoT and AI technologies, this system manages medical equipment throughout its lifecycle, optimizing equipment utilization, reducing operating costs, and improving overall hospital management.

[0006] The core architecture of the present invention includes the following four layers: 1) Device perception layer The device perception layer forms the foundation of the system and consists of smart sensors and RFID tags deployed on medical devices. These smart sensors, including temperature, humidity, vibration, and power consumption sensors, collect data on the device's operating status, such as temperature, humidity, vibration frequency, and energy consumption. RFID tags are passive tags operating at ultra-high frequencies (UHF, 860-960 MHz) and record the device's location and unique identifier in real time. Through these sensors and tags, the system can comprehensively perceive the device's operating status and usage, providing fundamental data support for subsequent data analysis and decision-making.

[0007] 2) Data transmission layer The data transmission layer utilizes 5G communication technology and an edge computing architecture to ensure real-time and reliable data transmission. The 5G communication module enables high-speed transmission of device data, supporting the simultaneous access and data transmission of large-scale devices. Edge computing nodes are deployed throughout the hospital's departments to locally process and cache device data, reducing data transmission pressure on the cloud and improving system response speed and data processing efficiency. Edge computing nodes also perform preliminary data analysis, such as device status monitoring and anomaly detection, generating real-time warning information.

[0008] 3) Data processing layer The data processing layer is the core of the system, applying deep learning and machine learning algorithms to conduct in-depth analysis of collected data. The equipment usage prediction module, based on a deep learning-based time series prediction model, analyzes historical usage data to predict future equipment usage demand, helping hospitals optimize resource allocation. The fault warning module uses machine learning algorithms to analyze equipment operating data, identify potential faults, and generate warnings, supporting preventive maintenance and reducing equipment downtime. The optimization scheduling module, based on a deep reinforcement learning network, enables efficient equipment deployment, reduces equipment idle time, and improves equipment utilization efficiency.

[0009] 4) Application Service Layer The application service layer provides a visual management interface and functional modules, supporting real-time monitoring, statistical analysis, maintenance reminders, and other functions for equipment. The real-time monitoring module displays the location, status, and usage of equipment through a visual interface, helping managers to understand equipment dynamics in real time. The statistical analysis module generates reports on equipment utilization, failure rate, maintenance costs, and other information, providing data support for management decisions. The maintenance reminder module generates maintenance plans based on equipment operating data and notifies relevant personnel via text message or email to ensure timely maintenance of equipment. The energy consumption monitoring module records and analyzes equipment energy consumption data, provides optimization suggestions, and reduces equipment operating costs. In addition, the application service layer also supports seamless integration with other hospital information systems (such as HIS, LIS, and PACS) to achieve data sharing and collaborative management.

[0010] The deep learning-based time series prediction model described is the Transformer model. The Transformer is a deep learning model based on a self-attention mechanism that captures global dependencies in time series and is used to process and predict time series data.

[0011] The machine learning algorithm is the XGBoost algorithm. XGBoost is an efficient machine learning algorithm based on gradient boosting. It can handle nonlinear relationships and high-dimensional data by constructing multiple decision trees and combining their prediction results.

[0012] The deep reinforcement learning network described is a deep Q-network (DQN). The goal is to learn an optimal Q-value function so that the action selected in each state can maximize the cumulative reward and learn the optimal strategy in a complex environment.

[0013] Other hospital information systems (such as HIS, LIS, and PACS) refer to core systems used to manage medical information, laboratory data, and imaging data within a hospital. The HIS (Hospital Information System) manages patient information, diagnosis and treatment processes, and hospital operational data; the LIS (Laboratory Information System) manages laboratory data and processes; and the PACS (Picture Archiving and Communication System) stores and manages medical imaging data.

[0014] The present invention has the following beneficial effects: Through equipment usage prediction and optimized scheduling, equipment utilization rate is significantly improved; through fault warning and preventive maintenance, maintenance costs and fault downtime are significantly reduced; through accurate equipment demand prediction and scheduling, equipment resource allocation is optimized to avoid duplicate purchases and waste of resources; through full life cycle management and data-driven decision support, the level of refinement and intelligence of hospital equipment management is improved; through real-time monitoring and safety hazard warning, the safe use of equipment is ensured and the risk of medical accidents is reduced; the system can be seamlessly integrated with the hospital's existing information system, and supports the expansion of functions and equipment types according to demand to adapt to the personalized needs of different hospitals.

[0015] Through the above method and algorithm, the present invention can realize intelligent management of medical equipment and provide hospitals with efficient, reliable and safe equipment management solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the perception layer structure of the embodiment device.

[0017] Figure 2 Schematic diagram of the data processing layer structure of the embodiment.

[0018] Figure 3 This is a schematic diagram of the application service layer structure of the embodiment. DETAILED DESCRIPTION

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The embodiments and conditions of the present invention are as follows: First, target equipment is selected in key departments of the hospital, such as operating rooms, ICUs, radiology departments, and laboratories, including monitors, infusion pumps, electrocardiographs, ultrasound machines, X-ray equipment, and MRI machines. Figure 1 As shown, each device is equipped with smart sensors (temperature, humidity, vibration, and power consumption sensors) and RFID tags (which record the device's location and unique identifier in real time). Sensor data is collected every 10 seconds, and RFID tags operate at an ultra-high frequency (UHF, 860-960 MHz) with a read range of 3-5 meters. The sensors and tags are connected to the data transmission layer via a wireless network, completing the construction of the device perception network.

[0021] Next, 5G communication base stations were deployed throughout the hospital to ensure signal coverage in all departments. Edge computing nodes were deployed in each department, equipped with a quad-core CPU, 16GB of memory, and 256GB of storage to support local data processing and caching. The edge computing nodes were connected to 5G communication modules to enable high-speed transmission and localized processing of device data. The 5G communication modules support concurrent connections for multiple devices, with transmission rates of 1 Gbps or higher. Data transmission protocols were configured to ensure real-time and secure data transmission.

[0022] Then, the hospital collected historical data such as equipment usage records, fault records, and maintenance records from the past two years, and cleaned, denoised, and normalized the data to ensure data quality. Figure 2 As shown, multiple models are used for data processing. The device usage prediction model is trained using a Transformer as a time series prediction model. The input is historical device usage data, and the output is device usage demand for the next week. The model is configured with a 6-layer encoder and decoder, with 512 neurons per layer. The training cycle is 200 epochs, using the AdamW optimizer and a cosine annealing learning rate scheduler. The fault warning model is trained using the XGBoost algorithm. The input is device operating status data (such as temperature, humidity, and vibration frequency), and the output is the failure probability. The model is configured with 200 trees, a maximum depth of 12, a learning rate of 0.1, and early stopping to prevent overfitting. The optimization scheduling model is trained using a DQN network. The input is device usage demand and location information, and the output is the optimal scheduling plan. The model is configured with a 4-layer fully connected network, with 256 neurons per layer. The training cycle is 500 epochs, using experience replay and a target network to stabilize the training process. The trained model is deployed to the cloud and edge computing nodes for real-time device data processing.

[0023] The application service layer provides a visual management interface and functional modules, such as Figure 3 As shown. The real-time monitoring module is based on a web-based visual interface and uses the ECharts library to implement dynamic chart display, showing the location, status and usage of the equipment in real time. The statistical analysis module generates reports such as equipment utilization rate, failure rate, maintenance cost, etc., and supports data export and visual analysis. The maintenance reminder module generates maintenance plans based on equipment operation data and sends notifications through SMS gateways and email servers, supporting scheduled tasks and instant triggering. The energy consumption monitoring module records and analyzes the energy consumption data of the equipment and makes optimization suggestions to help hospitals reduce energy consumption, optimize equipment utilization efficiency and reduce operating costs.

[0024] The implementation for seamless integration of the application service layer with the hospital's HIS, LIS, and PACS systems is as follows: First, a standardized API interface is developed, using a RESTful API or Web Service interface to transmit data in JSON or XML format, supporting two-way interaction between device management data and the HIS, LIS, and PACS systems. Second, real-time data synchronization is achieved through scheduled tasks or event-triggered mechanisms. Scheduled tasks are set to synchronize every hour, triggering synchronization in real time when the device status changes (such as a failure or maintenance completion). An MD5 checksum is added during the data synchronization process to ensure data integrity and consistency. Next, device status query and scheduling functions are embedded in the HIS system to support real-time query and scheduling of equipment by medical staff. Maintenance reminders and fault warnings are embedded in the LIS system to support maintenance and troubleshooting of laboratory equipment. Energy consumption monitoring and maintenance logging functions are embedded in the PACS system to support energy consumption optimization and maintenance management of imaging equipment. Through these methods, the application service layer is seamlessly integrated with the HIS, LIS, and PACS systems, providing the hospital with efficient, secure, and reliable equipment management support.

[0025] During operation, the system continuously collects device data, performs preliminary processing through edge computing nodes, and generates real-time information. Based on actual operational data, the system conducts monthly model evaluation and adjustments to optimize the LSTM model, random forest model, and genetic algorithm to improve prediction accuracy and scheduling efficiency. New functional modules or device types are added based on hospital feedback and needs. APIs are supported for accessing new device types and functional modules, supporting customized development.

[0026] After six months of system operation, the system's effectiveness was evaluated by analyzing metrics such as equipment utilization, maintenance costs, and downtime. Equipment utilization calculated the ratio of actual equipment usage time to total available time, maintenance costs calculated the total cost of equipment maintenance, and downtime calculated the downtime caused by equipment failures. A t-test was used to assess the significance of the differences before and after system implementation. The statistical results showed that the system significantly improved equipment utilization, reduced maintenance costs, and decreased downtime.

[0027] To ensure system security and compliance, AES-256 encryption is implemented for device and transmission data to prevent data leakage. Role-based permission management is implemented to ensure that only authorized personnel can access system data. System operation logs are recorded, including information such as operation time, operator, and operation content. These logs are retained for two years to facilitate traceability and auditing. Regular checks are conducted to ensure compliance with relevant regulations and standards for medical device management.

[0028] Through the above specific implementation methods, the present invention provides a comprehensive solution for the intelligent management of hospital medical equipment.

Claims

1. A hospital medical equipment management method based on the Internet of Things and AI, comprising the following steps: (1) Deploy smart sensors and RFID tags in various hospital departments to establish a device perception network to collect real-time data on the location, status, and usage frequency of medical equipment; (2) Build a data transmission layer, configure 5G communication modules and edge computing nodes to ensure the real-time and reliability of data transmission; (3) Training the algorithm models of the data processing layer, including the equipment usage prediction model based on deep learning, the fault warning model based on machine learning, and the optimization scheduling model based on deep reinforcement learning, to conduct in-depth analysis of the collected data to achieve equipment usage prediction, fault warning, and optimization scheduling; (4) Develop an application service layer to provide a visual management interface that supports real-time monitoring, statistical analysis, maintenance reminders, and energy consumption monitoring of equipment; (5) Continuously collect data during actual operation, optimize algorithm models, and improve system performance.

2. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The device perception layer includes smart sensors and RFID tags deployed on various medical devices, including: The RFID tag is a passive tag that operates at an ultra-high frequency (UHF, 860-960 MHz) and is used to record the device's location information and unique identifier in real time; The intelligent sensors include temperature sensors, humidity sensors, vibration sensors and power consumption sensors, which are used to collect the operating status data of the equipment.

3. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The data transmission layer includes: 5G communication module, used to achieve high-speed transmission of device data; Edge computing nodes are deployed in various departments of the hospital to perform local processing, caching and preliminary analysis of device data, reducing the pressure on cloud data transmission.

4. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The data processing layer includes: The device usage prediction module, based on the deep learning Transformer model, is used to predict the usage demand of devices in the future; The fault warning module uses the machine learning XGBoost algorithm to analyze equipment operation data, identify potential faults and generate warning information; The optimized scheduling module, based on the deep reinforcement learning DQN network, achieves efficient equipment deployment and reduces equipment idle time.

5. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The application service layer includes: Real-time monitoring module, which displays the location, status and usage of the equipment through a visual interface; Statistical analysis module, generating reports on equipment utilization rate, failure rate, maintenance cost, etc.; Maintenance reminder module, which generates maintenance plans based on equipment operation data and notifies relevant personnel via SMS or email; The energy consumption monitoring module records and analyzes the energy consumption data of the equipment and makes optimization suggestions.

6. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The system further comprises: The asset lifecycle management module covers equipment procurement, use, maintenance, and retirement, and records equipment data throughout its lifecycle. The safety monitoring module analyzes equipment operation data to identify safety hazards and generate early warning information; The interface module supports seamless integration with hospital information systems (HIS, LIS, PACS) to achieve data sharing and collaborative management.

7. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The described method is applicable to the management of the following medical devices: Vital signs monitoring equipment, including multi-parameter monitors, ECG monitors, and fetal heart rate monitors; Therapeutic equipment, including infusion pumps, syringe pumps, ventilators, and anesthesia machines; Diagnostic equipment, including electrocardiographs, ultrasound machines, X-ray machines, CT scanners, and MRI machines; Operating room equipment, including surgical lights, operating tables, electrosurgical units, and endoscopy systems; Laboratory equipment includes biochemical analyzers, blood cell analyzers, and urine analyzers.

8. The hospital medical equipment management method based on the Internet of Things and AI according to claim 1 is characterized in that: The method is scalable and supports the addition of new functional modules or equipment types according to hospital needs through modular design, including customized development through API interfaces or plug-in mechanisms.

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