Hospitals worldwide recognise the importance of data and digital transformation in healthcare. We traced a smart hospital's data-driven journey to build an artificial intelligence and digital ecosystem (AIDE) to achieve healthcare excellence. We measured the impact of data and digital transformation on patient care and hospital operations, identifying key success factors, challenges, and opportunities. The use of data analytics and data science, robotic process automation, AI, cloud computing, Medical Internet of Things and robotics were stand-out areas for a hospital's data-driven journey. In the future, the adoption of a robust AI governance framework, enterprise risk management system, AI assurance and AI literacy are critical for success. Hospitals must adopt a digital-ready, digital-first strategy to build a thriving healthcare system and innovate care for tomorrow.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Hospitals ; Cloud Computing ; Robotics ; Internet of Things ; Data Science

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

OBJECTIVE: This study aims to address the configuration and efficiency issues in the use of digital subtraction angiography (DSA) equipment through the practical implementation of a rationalization platform based on the Internet of Things (IoT). METHODS: By employing IoT and data integration technologies, the deep integration of DSA equipment operational data with clinical data was achieved to construct a knowledge base for rational use of DSA equipment. Simultaneously, a knowledge base was developed using software engineering techniques to visually display data analysis results. RESULTS: Through thorough data analysis, an imbalance in DSA usage between the southern and northern hospital campuses was identified. Addressing this issue, optimizations were implemented based on the data analysis results, which ultimately yielded significant effects. These adjustments not only effectively alleviated the pressure on DSA equipment usage in the southern campus, but also increased equipment utilization in the northern district (the average daily working hours have increased from 4.64 h to 7.19 h), shortened patient appointment wait time (the appointment duration in the southern campus decreased by 21.86% year-on-year, while the appointment duration in the northern campus decreased by 20.51% year-on-year). CONCLUSION Through the practical implementation of a DSA rationalization platform based on IoT, this study not only successfully explored methods for rational DSA usage but also provided valuable reference for the rational management of medical equipment.
Internet of Things ; Angiography, Digital Subtraction/instrumentation* ; Humans ; Software

Medical equipment, as an important indicator of smart hospital evaluation, plays a vital role in hospital operations. To ensure the safe and efficient operation of medical equipment, a reasonable performance evaluation system is indispensable. This study introduces a platform based on Internet of Things (IoT) technology that connects medical devices and collects data, achieving standardized and structured data processing, and supporting online operational supervision. Through the Delphi method, a performance evaluation system for large medical equipment is constructed, including 4 primary indicators and 22 secondary indicators. DICOM data acquisition devices are used to achieve functions such as efficiency analysis, benefit analysis, usage evaluation, and decision-making support for medical equipment. The study is still in its early stages, and in the future, it is expected to integrate more types of equipment, achieve rational resource allocation, and significantly impact decision-making for the development of public hospitals.
Internet of Things ; Delphi Technique

Medical equipment management plays a crucial role in enhancing the quality and efficiency of healthcare services. However, traditional management approaches are increasingly inadequate to meet the growing demands of modern healthcare. As intelligent operation and maintenance (O&M) models based on big data, the Internet of Things (IoT), and artificial intelligence (AI) technologies develop, it is imperative to explore their application in medical equipment management. This paper reviews the technical overview of intelligent O&M and discusses the algorithms and challenges of intelligent O&M models based on different technologies. It also proposes issues that need improvement in intelligent O&M models, aiming to provide valuable references for the future development of medical equipment management.
Artificial Intelligence ; Algorithms ; Internet of Things ; Equipment and Supplies ; Big Data

At present, there are disadvantages with the detection for occupational hazard factors, such as insufficient monitoring data, poor timeliness, weak representativeness, long detection cycles, and inability to continuously monitor. Taking advantages of internet of things technology, an online monitoring platform for occupational hazard factors has been designed. The platform collects the concentration (intensity) of hazard factors through sensors, transmits the occupational hazards data collected online in realtime. The online monitoring cloud center for occupational hazard factors processes and analyzes online monitoring data in realtime, stores the hazard factors data to form database management, and provides user application services to form an intelligent online monitoring service model for occupational hazard factors. Based on the online monitoring platform of occupational hazard factors, multi-level government health supervision departments and employers can grasp the status of hazard factors in real time, which is conducive to improving the level of occupational hazard supervision.
Internet of Things ; Internet

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Internet of Things ; Reproducibility of Results ; Internet ; Machine Learning ; Technology

Internet of Things plays a vital role in the field of healthcare. Smart medical devices, innovative sensors and lightweight communication protocols are making the Internet of Medical Things possible. This paper summarizes the research progress of Internet of Things technology in medical engineering from two aspects of health monitoring system and ingestible sensor monitoring equipment. The health monitoring system is analyzed from heart disease monitoring, diabetes monitoring and brain nerve monitoring. The medical equipment that can absorb sensors is represented by capsule endoscope. This paper further summarizes the relevant situation of smart hospital, and finally discusses the challenges and countermeasures of the Internet of Things technology in medical engineering, in order to lay the foundation and provide ideas for the research of the Internet of Things technology in medical engineering.
Internet of Things ; Technology ; Internet ; Brain ; Communication

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet

OBJECTIVE: To discuss how to implement and promote the lean management of medical devices life cycle through establishing medical device UDI. METHODS: Discuss the application of UDI on medical consumables lean management by relying on the construction of medical device UDI system in our country, and summarize the pilot experience of implanted medical devices in Shanghai. RESULTS: Improve the application value of UDI in medical device Internet of Things through analyzing the present situation of implanted medical device and further strengthen the UDI application standardization. CONCLUSIONS Form the interconnection and mutual recognition during medical device life cycle and reduce the input of product cost and resource consumption in our country through establishing a more standardized and effective UDI circulation system.
China ; Internet ; Internet of Things ; Prostheses and Implants