Remote patient monitoring (RPM) has become an integral part of modern healthcare, using wearable devices to track patients’ conditions outside the clinic or hospital environment. 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 proactively identify conditions.
Because of the technology’s inherent benefits, 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 handling these large amounts of data. It processes data at the source instead of sending it to remote locations, ensuring faster speeds and improved data security. The edge computing in healthcare market, in terms of revenue, was estimated at $4.1 billion in 2022 and is expected to increase to $12.9 billion by 2028, according to a recent report by MarketsandMarkets.
As edge computing technology becomes more prominent, it is critical to properly design workflows to take advantage of faster processing near the data source and minimize latency and workload in the cloud. For example, some data at the edge never needs to be sent to the cloud, while others can be transferred retroactively or in bulk, which does not require real-time processing, which is network and computing intensive.
The evolution of Edge Computing in RPM
Traditional cloud models are limited in their ability to handle significant volumes of data in real time. Bandwidth limitations can cause transmission delays for critical patient information, hindering rapid analysis and prompt treatment. Additionally, storing sensitive medical data on remote cloud servers can create data protection concerns.
Edge computing benefits are particularly evident for RPM computing. With its rapid analysis of critical patient data, healthcare professionals can make quick decisions and intervene. By processing data closer to its origin, edge computing minimizes network congestion, improving overall data processing speed. It also improves patient data privacy by keeping sensitive information closer to the source, reducing exposure to potential security breaches.
In addition, edge computing facilitates real-time data validation and preprocessing at the source, enabling immediate detection and response before data is transferred to the central cloud infrastructure. With this capability, edge computing improves data accuracy and addresses practical processing challenges, ultimately improving data quality and reliability for analysis.
Extend reach and accessibility in RPM
With the development of advanced computing technology in wearable devices, patients benefit not only from improved convenience, but also from a deeper level of engagement in their healthcare.
Edge computing plays a critical 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 geographic limitations. The result is a more diverse participant demographic, enriching health care insights and research findings.
By leveraging edge computing, RPM can extend its reach beyond geographic boundaries, improve patient outcomes, and increase access to healthcare. As a result, previously underserved patient populations, including those in rural areas, can benefit from remote monitoring and rapid response.
New biomarkers drive the need for Edge Computing
Important advances in RPM, such as continuous monitoring and real-time data streaming, have driven the technology’s increasing use 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 significant computational resources, often exceeding the capacity of conventional computing systems. This is even more important when providers are monitoring patients in real time and need rapid insights for diagnosis. New biomarkers are useful for faster and more accurate disease identification, but efficient data processing is essential.
Edge computing optimizes validation and integration of biomarkers by efficiently distributing computational tasks to devices equipped with edge computing capabilities, such as wearables and mobile phones. It minimizes latency, speeds up analysis and ensures fast results for effective medical interventions. In addition, edge computing can streamline the integration of biomarker algorithms into RPM devices, making advanced diagnostic tools more efficient.
Design considerations for the future of wearable technology
Incorporating edge computing technology into RPM devices is a game changer for efficient data management. Designers play a critical role in maximizing the full potential of Edge Computing to ensure seamless collection, processing and transfer of patient data.
When designing for edge computing in RPM, here are some questions to consider:
- Does data need immediate attention at the edge?
- Is the data only used for local processing and therefore not needed in the cloud?
- Can 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 improved storage and connectivity 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 gives healthcare providers instant access to critical patient data for accurate diagnoses, personalized treatments and early intervention. Its ability to handle large volumes of real-time data increases data reliability, increases 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 to market. Li’s nearly 20-year high-tech career took a new turn when a routine health check landed him in the emergency room under investigation for fear he was in the middle of a heart attack. Noticing the outdated monitoring technology at the hospital, he knew that emerging technologies could be properly implemented and sought to apply his background in flexible electronics to healthcare. Before joining Vivalink, he was responsible for the development of new products and technologies as VP of engineering at Kovio and Thinfilm Electronics, leading printed electronics companies. Prior to that, he worked at AMD and the AMD/Fujitsu joint venture, Spansion. As VP of product development at Spansion, Jiang managed the major new product launches at Spansion. Jiang holds a Ph.D. degree from the University of Wisconsin-Madison, and a bachelor’s degree from Zhejiang University in China.
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