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  • Summer Atlantic

The Internet of Medical Things (IoMT), with Broad Growth Potential


From 1970 to 2000, information systems in healthcare primarily focused on logistics, organization, and management. From 2000 to 2020, medical information systems mainly relied on the application of structured static data. After 2020, with medical devices becoming more sophisticated and network communication more convenient, the concept of real-time, efficient, secure, and reliable medical Internet of Things (IoT) emerged.

 

Terminal Composition and Network Architecture

Terminal Composition

Devices Types

Examples

Characteristics

Large Imaging Devices

MRI, CT, PET-CT, PET-MR

Non-destructive medical imaging

Dynamic with high bandwidth and not sensitive to latency

Require for real-time transmission of connected imaging data Can reach several hundred megabits

Vital Signs Monitoring Devices

monitors, ventilators

Real-time monitoring of patients' vital signs

Streaming data

Requiring bandwidth within 10 Mbps, and is sensitive to latency

Wearable Devices

watches, oximeters

Require bandwidth within 1 Mbps

Latency-sensitive and packet-loss sensitive

Auxiliary Service Devices

medication, medical consumables, and valuable assets

Bandwidth requirements at the kilobit level

Not too sensitive to latency

Intelligent Devices

operational robots, automatic medication dispensing robots, delivery robots

Require highly reliable connection application systems to Provide Always-on services. dynamic, with bandwidth within 10 Mbps

Sensitive to latency and packet loss

Network Architecture

Application Scenarios

In hospital ward nursing scenarios, millimeter-wave sensing technology is used to achieve contactless posture detection and vital signs sensing. For patients' in-bed/out-of-bed status, the accuracy exceeds 99%; for detecting falls and other movement behaviors, the accuracy exceeds 95%. Through this sensing technology, medical staff can monitor patients' falls, breathing, and heart rate data in real-time, improving the efficiency of medical staff and patient satisfaction.

 

In more detail, infusion monitoring terminals collect real-time data on the infusion status of each bed, such as flow rate, remaining infusion volume, and remaining time. This data is transmitted to a central station through a wireless IoT network. Based on threshold values, the system intelligently alerts medical staff, reducing the time spent on infusion patrols and enhancing patient satisfaction. Additionally, infusion monitoring can be integrated with vital signs monitoring information to oversee infusion risks and prevent medical infusion accidents.

 

Challenges Encountered and Solution

Regulations

There is an increased focus on privacy and security in the area of big data security for individuals. Whether it's the Health Insurance Portability and Accountability Act (HIPAA) in the United States or the General Data Protection Regulation (GDPR) in the European Union, both start from the perspective of individual information privacy and security. They impose requirements on businesses that store and use data, to ensure the rights of the data subjects.

 

Hardware and Network Communication

There's no unified IoT interface standard for medical devices, leading to a wide variety of APIs or proprietary protocols like HL7, Benelink by Mindray, and MEDIBUS by Dräger. This results in different interface protocols for data extraction from medical devices, creating a barrier between devices and applications. The lack of interoperability, the dispersed placement of medical devices across the hospital and the varied security authentication capabilities of these devices risks the overall network security when data is transferred to data center applications.

 

Automation

As we are still in the process of informationization, a large number of medical IoT devices and equipment were still not connected to the network. Some patient test and examination data had to be manually entered into the hospital information system by medical staff. Manual collection was prone to errors, latency, and failed to holistically record patients' comprehensive multi-dimensional medical data. The automation of data collection is a must for AI.

 

In large-scale IoT environments, it is impossible to achieve operations and maintenance management manually. Instead, the adoption of automated and intelligent technological means is necessary. This includes monitoring the entire network's topology objects, configuring network elements, monitoring services and performance, diagnosing faults, viewing resources, and generating reports.

 

Fragmentation

Without top-down planning and design, the essential infrastructure for smart hospitals—the healthcare IoT—suffers from isolated systems and a fragmented network, making it difficult to sustain the smart hospital's advancement and growth.

 

Acquisition

A significant barrier in enhancing data analysis is the difficulty in accessing valuable data, often due to concerns related to business protection and personal privacy. Both equipment manufacturers and patients tend to be hesitant about sharing data, due to lack of mutual trust and mutual benefit mechanisms.

  

Possible Solution

The application of cloud computing in the healthcare sector is expected to continue increasing. Along with this, the amount of Private Health Information (PHI) stored in the cloud will also keep growing. For cloud service providers, it is crucial to focus on several issues. For instance, whether the purposes of collecting, using, maintaining, and sharing PHI are described in privacy notices, whether there are privacy roles, responsibilities, and access requirements for contractors and service providers, whether privacy controls and internal privacy policies are monitored and audited, and whether it is confirmed that the collection of PHI is limited to the minimum necessary to fulfill the purpose of lawful authorization, among others. From a coordination perspective, a neutral third party within the industry can establish a data sharing ecosystem, ensuring that all parties involved benefit, thereby encouraging voluntary participation in data sharing.

 

APAC Potential

The APAC loTHealthcare Market size is forecasted to be USD 61.68 bn by 2028 and grow at a CAGR of 22.28% between 2023 to 2028. Below is an analysis by Data Bridge.

In APAC, the combined growth of developed IoT regions (such as South Korea, Japan, Australia, and China) and the emerging IoT regions (such as India, Pakistan, Bangladesh, Indonesia, and Thailand) are pushing the 14.5 billion IoT devices in circulation today to 38.9 billion in 2030.

 

The factors affecting the IoT healthcare market include consumption volumes, production sites and volumes, import-export analysis, price trend analysis, cost of raw materials, downstream and upstream value chain analysis, and changes in domestic market regulations, which are the primary indicators used to forecast the market scenarios. Furthermore, in conjunction with tariffs and trade routes, competition between international and local brands should also be taken into account.

 

Digital development in the Asia-Pacific region is characterized by disparities and connectivity challenges, with certain countries leading global advancements while others remain in nascent stages. The lack of skilled workforce and technological expertise to deploy IoT solutions in underdeveloped economies presents a significant growth restraint for the market. Moreover, the high costs associated with IoT infrastructure development, along with increasing concerns regarding privacy and data security, are expected to further impede the growth rate of the market. Additionally, the lack of awareness among the public in developing regions will also serve as a barrier to market growth. In addition, countries like Indonesia, Thailand, and Vietnam are composed of many individual islands. One single connectivity technology cannot possibly fit all the local nuance and requirements to dominate the IoT space. However, industrial automation and widespread internet access are a necessity.

 

National governments can make great difference. China push towards NB-IoT which heavily influences the overall APAC view, and examples also include the move towards 6G. Whilst 5G networks are still rolling out in some markets, Singapore has launched a Future Communications Connectivity Lab to research and develop 6G technology. South Korea is the region’s leader on 6G, aiming to commercialize services by 2030. Singapore has introduced the Cybersecurity Labelling Scheme to improve IoT device security general cyber hygiene. The Korea Internet & Security Agency (KISA) released guidelines that emphasizes privacy-by-design principles to IoT device manufacturers; also including tips on personal information protection, such as conducting data breach risk checks before launching new services. Japan’s Ministry of Economy, Trade and Industry (METI) has Introduced an international standard to facilitate the development and maintenance of IoT services/products, based on the country's IoT safety and security guidelines.

 

Overall, the Asia-Pacific region presents a promising landscape for the growth of medical IoT, with opportunities for innovation, collaboration, and improved healthcare outcomes across diverse healthcare settings.

 

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