Remote patient monitoring (RPM) has become an integral part of modern healthcare, utilizing wearable devices to track patients’ conditions outside the clinic or hospital setting. Devices like these collect critical information from patients, such as their heart rate, activity levels, and temperature, which providers can use for real-time insights to guide treatment and identify conditions proactively.
Due to the technology’s inherent advantages, providers and researchers have increasingly embraced wearables, generating an influx of health-related data. However, traditional cloud computing struggles to handle increased data volumes, resulting in slower processing speeds and potential data loss.
Edge computing is the key to managing these large quantities of data. It processes data at the source rather than sending it to distant locations, ensuring faster speeds and improved data security. Edge computing in the healthcare market, in terms of revenue, was estimated at $4.1 billion in 2022 and is projected to surge to $12.9 billion by 2028, according to a recent report by MarketsandMarkets.
As edge computing technology becomes more prominent, it is crucial to properly design workflows in order to take advantage of more timely processing near the source of the data as well as minimizing latency and workload on the cloud. For example, some data at the edge never needs to be sent to the cloud, while others may be transferred retroactively or in bulk, thereby not requiring real-time processing, which is network and computing-intensive.
The Evolution of Edge Computing in RPM
Traditional cloud computing models are limited in their ability to handle substantial real-time data volumes. Bandwidth limitations can cause transmission delays for critical patient information, hindering timely analysis and prompt treatment. Plus, storing sensitive medical data on remote cloud servers can generate concerns about data protection.
Edge computing’s advantages are particularly evident for RPM data processing. With its rapid analysis of critical patient data, healthcare professionals can make timely decisions and interventions. By processing data closer to its origin, edge computing minimizes network congestion, improving overall data processing speeds. It also enhances patient data privacy by retaining sensitive information closer to its source, reducing exposure to potential security breaches.
On top of that, edge computing facilitates real-time data validation and preprocessing at the source, allowing immediate detection and response before transmitting data to the central cloud infrastructure. With this capability, edge computing enhances data accuracy and addresses practical processing challenges, ultimately improving data quality and reliability for analysis.
Expanding Reach and Accessibility in RPM
With the advancement of edge computing technology in wearable devices, patients benefit not just from improved convenience, but also from a deeper level of engagement in their healthcare.
Edge computing plays a crucial role in expanding RPM’s reach by enabling remote patient participation and broadening the candidate pool for clinical trials. Researchers can engage individuals previously excluded due to geographical constraints. The result is a more diverse participant demographic, enriching healthcare insights and research outcomes.
By leveraging edge computing, RPM can extend its reach beyond geographical boundaries, improving patient outcomes and increasing access to healthcare. As a result, previously underserved patient populations, including those in rural areas, can benefit from remote monitoring and timely interventions.
New Biomarkers Driving The Need For Edge Computing
Key advancements in RPM, such as continuous monitoring and real-time data streaming, have fueled the technology’s increasing adoption and are likely to contribute to the discovery of new biomarkers. The emergence of new biomarkers increases the demand for efficient and sophisticated data processing methods.
The analysis of such biomarkers requires substantial computational resources, often surpassing the capabilities of conventional data processing systems. This is even more critical when providers monitor patients in real time and need quick insights for diagnosis. New biomarkers are useful for faster and more accurate disease identification, but efficient data processing is essential.
Edge computing optimizes biomarker validation and integration by efficiently distributing computational tasks to devices equipped with edge computing capabilities, such as wearables and mobile phones. It minimizes latency, accelerates analysis, and ensures prompt results for effective medical interventions. Plus, edge computing can streamline the integration of biomarker algorithms into RPM devices, making advanced diagnostic tools more efficient.
Design Considerations for Future of Wearable Technology
Incorporating edge computing technology into RPM devices is a game-changer for efficient data handling. Designers play a pivotal role in maximizing edge computing’s full potential to ensure seamless collection, processing, and transmission of patient data.
When designing for edge computing in RPM, here are some questions to consider:
- Does the data need immediate attention at the edge?
- Is the data only used for local processing, and therefore not needed in the cloud?
- Can the data be sent to the cloud retroactively or in bulk, versus in real time?
As the volume of data generated by RPM continues to expand, the demand for enhanced storage and connectivity capabilities is critical. Edge computing optimizes local data storage for immediate access and processing. Designers can focus on integrating storage solutions that align with the device’s edge processing capabilities, enabling efficient data storage and retrieval.
As RPM demand increases, traditional cloud computing limitations highlight the need for innovative solutions. Edge computing empowers healthcare providers with instant access to critical patient data for accurate diagnoses, personalized treatments, and early interventions. Its capacity to handle large volumes of real-time data boosts data reliability, enhances patient engagement, and improves accessibility.
About Jiang Li
Jiang Li, founder and CEO of Vivalink, has both passion and extensive experience in bringing innovative technology and products into the marketplace. Li’s nearly 20-year career in high technology took a new direction when a routine health check landed him at the ER under examination out of fear he was in the middle of a heart attack. Noticing the outdated monitoring technologies in the hospital, he knew emerging technologies could be properly implemented and sought to apply his background in flexible electronics to healthcare. Prior to joining Vivalink, he was responsible for new product and technology development as the VP of engineering in Kovio and Thinfilm Electronics, leading printed electronics companies. Prior to that, he worked at AMD and the joint venture between AMD/Fujitsu, Spansion. As the VP of product engineering in Spansion, Jiang managed the major new product launches in Spansion. Jiang holds a Ph.D. degree from the University of Wisconsin-Madison, and a bachelor’s degree from Zhejiang University in China.
