New Media, Cloud Computing, and Fog Computing in Healthcare

- Overview

In healthcare, "Cloud Computing" refers to storing and processing medical data on remote servers accessible via the Internet, allowing healthcare providers to access patient information from anywhere, while "Fog Computing" is a decentralized approach where data is processed closer to the source (like medical devices) instead of sending everything to a central cloud, enabling faster real-time analysis for time-sensitive medical situations; essentially, Fog Computing acts as a middle layer between the devices and the cloud, reducing latency and improving responsiveness. 

Key differences: 

• Data Processing Location: Cloud computing processes data in a centralized data center, while Fog computing processes data at the network edge, closer to where it is generated.
• Latency: Due to the distance data travels, Cloud computing can experience higher latency, while Fog computing offers lower latency for time-critical applications.
• Applications: Cloud computing is suitable for large-scale data storage and analysis, while Fog computing is ideal for real-time patient monitoring using wearable devices or medical sensors.

Examples of Cloud Computing in Healthcare:

• Storing patient medical records on a remote server for easy access by healthcare providers.
• Sharing patient data between different healthcare facilities
• Running complex data analytics on large patient datasets

Examples of Fog Computing in Healthcare:

• Processing live data from a patient's wearable heart monitor to alert caregivers about abnormal readings in real-time
• Analyzing data from an in-hospital monitoring system to detect potential complications early
• Performing initial data analysis on medical devices before sending a summarized version to the cloud

New Media and Digital Healthcare Transformation

Modern healthcare is being transformed by new and growing electronic resources, with hospitals generating terabytes of imaging, diagnostic, monitoring, and treatment data. Machine learning (ML) is central to utilizing these rapidly expanding datasets, combing through data across patients, clinics, and hospitals to uncover more effective treatments and practices that increase the quality and longevity of human life.  

The rise of new media has increased communication between people all over the world and the Internet. It allows people to on-demand (cloud computing) access to content anytime, anywhere, on any digital device, as well as interactive user feedback, and creative participation. 

New media allows the real-time generation of new, unregulated content, including (at least for now) Internet, blogs, websites, computer multimedia (e.g., medical audio or speech, real-time or recorded video, high resolution still image, and so forth), pictures, and other user-generated media. 

It is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and Internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. New media will have major impact on healthcare delivery and, perhaps, on costs as well. 

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- Healthcare and Cloud Computing

Pushing computing, control, data storage and processing into the cloud has been a key trend in the past decade. However, cloud alone is encountering growing limitations in meeting the computing and intelligent networking demands of many new systems and applications. 

Local computing both at the network edge and among the connected things is often necessary to, for example, meet stringent latency requirements, integrate local multimedia contextual information in real time, reduce processing load and conserve battery power on the endpoints, improve network reliability and resiliency, and overcome the bandwidth and cost constraints for long-haul communications. 

The cloud is now "descending" to the network edge and sometimes diffused onto end user devices, which forms the "Fog". Fog computing is a service-oriented intermediate layer in the Internet of Things (IoT), providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. 

Examples of Cloud Computing in Healthcare:

• Storing patient medical records on a remote server for easy access by healthcare providers.
• Sharing patient data between different healthcare facilities
• Running complex data analytics on large patient datasets

- Healthcare and Fog Computing

Fog computing will change the information technology industry in the next decade. It enables key applications in wireless 5G, IoT, and big data. Fog computing and networking present a new architecture vision where distributed edge and user devices collaborate with each other and with the clouds to carry out computing, control, networking, and data management tasks. 

The IoT may more likely be supported by fog computing, in which computing, storage, control and networking power may exist anywhere along the architecture, either in data centers, the cloud, edge devices such as gateways or routers, edge equipment itself such as a machine, or in sensors. 

Fog computing distributes the services of computation, communication, control and storage closer to the edge, access and users. The centerpiece of fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. 

Examples of Fog Computing in Healthcare:

• Processing live data from a patient's wearable heart monitor to alert caregivers about abnormal readings in real-time
• Analyzing data from an in-hospital monitoring system to detect potential complications early
• Performing initial data analysis on medical devices before sending a summarized version to the cloud