Technology has transformed the way we make diagnoses, patient care, and treatments. Edge computing and 5G accelerate innovation to make healthcare faster, cheaper, and better. During an epidemic, many people don't want to go to the hospital, which impacts their early diagnosis and later recovery. In addition, the steady growth of the elderly population is putting increasing pressure on senior care facilities around the world.
The number of people who need fast, efficient care far exceeds the number of providers available to care for them. According to the World Health Organization, we face a global shortage of 7 million health workers. That's bad news. The good news is that rapid advances in technologies such as edge computing and 5G are making it easier to introduce solutions that can make up for the labor shortage and transform the way healthcare is delivered.
Let's take a look at some of the healthcare use cases for edge computing and 5G:
Wearable
Wearable devices allow continuous monitoring of blood pressure, heart rate, temperature, oxygen levels, and more.
This data is pushed to the nearest edge server and processed locally at that edge location, minimizing latency and increasing processing speed. Doctors can use this information to assess a patient's health in real time.
In the hospital
Radar-enabled bedside sensors monitor vital signs such as heart rate, respiratory rate, and blood pressure, while alerting caregivers when normal limits are exceeded.
The bed sensors track how long people sleep. Data from sleep patterns can detect early signs of disease.
Until recently, hospitals had a centralized architecture with data stored in the cloud. A variety of smaller clinics and medical centers were connected to a central location to store and process data.
Edge computing gives hospitals the benefit of storing data at the nearest edge location and enabling it to be processed quickly. This has an added security benefit because the data is stored locally and not transmitted over long distances to the cloud, which reduces the risk of someone hacking into the data midway.
Point-of-care diagnostics and telemedicine
On-demand healthcare in the form of mobile point-of-care diagnostics brings healthcare to people in both urban and rural areas.
Along with vital signs, disease-specific data for key conditions such as diabetes and cardiovascular disease is pushed to the nearest edge server. This data can be processed, analyzed, and transmitted to a physician in a remote location within minutes.
The rapid availability of health data has led to the growth of remote health applications, resulting in increased capacity requirements at service provider sites.
Edge computing helps developers quickly add additional compute and storage capacity to meet urgent demands and optimize resources.
Ambulances
Ambulances can now do more than just transport patients to and back from the hospital.
Technology embedded in point-of-care screening equipment and HD video can transmit data on vital signs and other health parameters over 5G connectivity to a central monitoring station and back over the last-mile edge provider's network.
Paramedics and emergency medical responders can then work with doctors to stabilize patients before they are transported to the hospital, while emergency room staff can prepare for the patient's arrival.
Artificial Intelligence
No discussion of the future of healthcare would be complete without artificial intelligence. From chronic disease and cancer to radiology and risk assessment, we can use AI to transform patient care and diagnosis.
Here are two examples of how AI can transform early detection and diagnosis.
Melanoma detection - Melanoma is a malignant tumor that accounts for more than 70% of skin cancer-related deaths. Doctors often rely on visual inspection to identify suspicious skin lesions. Although in many cases it is difficult to make an accurate diagnosis.
Artificial intelligence can help solve this problem. Software systems using DCNNs (Deep Convolutional Neural Networks) can analyze wide-angle images acquired by smartphone cameras and identify lesions that need further investigation.
By storing the images at the nearest edge server and processing them locally, the results are returned within minutes.
Radiology - Edge computing generates AI applications that reduce the time required for MRI scans. new research from Facebook and NYU Grossman School of Medicine shows that AI-generated, fast MRI images contain diagnostic information that is comparable to images captured by slower traditional MRI scanners comparable to those taken by slower conventional MRI scanners.
By removing roughly three-quarters of the raw data used to create the scans, the AI model was able to generate fastMRI scans that were comparable to the fast MRI scans created by the normal MRI process.
Because fastMRI scans require four times less data, they have the potential to scan patients faster, thereby reducing the amount of time they spend on the MRI machine.
Edge and 5G go hand in hand
It's a mistake to think about 5G or edge computing in isolation. Edge computing is the only way for 5G to achieve the goal of less than 5 milliseconds of latency. While most people think of 5G as having lower latency, they forget the amount of data generated by edge devices.
Devices such as wearables, sensors, and other IoT devices generate large amounts of data that need to be managed and processed locally, and the results transmitted back to doctors, hospital emergency rooms, and remote facilities in near real-time.
However, this is not always possible due to the less-than-optimal routing infrastructures of many telcos, and 5G with edge promises to solve this problem by dramatically reducing latency between endpoints and data centers.