Edge Computing in Healthcare

Every time a new technology Y comes into space where an old technology X is dominant, we always encounter “X is dead, Y is the future”. We’ve seen this on a number of occasions. Data warehousing, BI, application development, etc. have, at various points of time been described “dead”. And then we have cloud computing, a relatively new concept if you look at the history of computing. But it’s rather old news in the current pace of technology.

Edge computing refers to computations and data analyses done near the source of data. In cloud computing, the data was sent to data centers for computation and the newly compiled data was sent back to the data source. Edge computing is for real-time cloud analytics that has to be applied immediately within devices, facilities or applications. We live in an era where billions of devices running and processing data, so it becomes vital to filter data within the data source. Now, cloud computing and edge computing work in tandem. In cases of Industrial Automation, there is a high necessity for quick and sharp actions within the supply chain.



Since the essence of edge computing is its ability to reduce the time between data capture and critical analysis, it has a number of prospective use-cases in the healthcare industry :

  • Remote Outreach: Since it’s difficult for patients in remote and rural areas to get affordable and quality healthcare services, edge computing can help devices monitor patient data real-time. Even if a patient had access to a professional, they would require access to immediate data for treatment.

  • Detailed medical records: Take the case of a patient suffering from epilepsy. The number of seizures, jerk rates, heart rate, etc. can be determined by the patient themselves through different software. Depending on these patterns, there can be proactive treatment changes.

  • Better Response Times Perhaps the greatest benefit of edge computing is quicker responses and proactive medical attention. A patient inside a hospital is connected with a number of monitoring devices, say, 30 per bed. In a big hospital, with so many medical devices and connections over the network, there is a huge burden on response time. With edge computing, patients can receive quicker treatments with more focused attention on data thanks to the distribution of computing services on the network.

  • Wearables and Embedded devices: People undergoing treatment for cancer, diabetes, etc., can access wearable devices like watches or wristbands to track activity levels, food and exercise habits, and fatigue levels. This can also be used to help patients and doctors alike to switch between monitoring environments. For example, if the heart rate becomes significant to a discharged patient’s treatment, wearables help doctors and patients keep a constant check on it and address any abnormalities.

  • Cost Savings: With sensors, smart surveillance and interconnected medical devices there is bound to be a drastic reduction in operational costs. Also, there is the added benefit of improved record-keeping. In the current system, there are a bunch of devices transmitting high volumes of raw data. With edge computing, we’re looking at data filtration at the edge of the network itself and hence, a streamlined approach towards getting more meaningful insights with lesser amounts of data. Also, data center power, maintenance, and other costs will be reduced substantially.

From here on, edge computing will prevail with cloud computing for sure. The only bottleneck that needs to be addressed is how ready are healthcare chains to incorporate such a form of technology. Only time will tell, but we are heading towards very exciting times indeed!