Different Aspects of Machine Learning for Healthcare Technologies

Machine Learning for Healthcare Technologies is a subset of machine learning that has gained a lot of attention in the last decade. Machine Learning for Healthcare Technologies focuses on the ways machine learning can be applied to healthcare in various aspects like healthcare data storage, diagnosis, treatment plans, clinical trials, and even healthcare administrative activities.

 

Machine Learning for Healthcare Technologies (IoT)

One subset of machine learning is the Internet of Things. Put simply, the internet of things is the ability of machines to communicate with each other. These machine to machine automated activities can carry out complicated tasks that are otherwise labor-intensive or time consuming for healthcare staff. The internet of things automation also helps to generate big data that can be used for process optimization and automation to save time and minimize waste. The Internet of Things technology is used in several industries and so there’s no reason why the healthcare industry cannot and should not adopt it for its benefits. For example, in the banking industry, the internet of things technology was used to gather big data on ATM withdrawals in different locations and almost precisely predict the amount of cash would be needed at certain periods of time. This helped solve the problem of cash shortages in ATM machines. This can be applied to healthcare in areas such as bed availability, length of stay, nurses to patient ration optimization etc. Internet of things devices also gathers and store loads of data which allows for deep data analytics that opens the door to more possibilities. Big data can be analyzed for trends, patterns, anomalies, insights, and outliers, that could offer insight into how a healthcare organization operates. Big data helps with statistical analyses and research that could help improve healthcare and take the industry into another level of innovation.

Internet of Things for Healthcare Technologies

A huge part of machine learning in healthcare and the Internet of Things technology for healthcare is the use of wearable health tracking devices by patients. The possibilities brought forth by wearable devices are huge and very beneficial to patient care. Wearable devices allow doctors to track the health of patients in real time which can be very beneficial for patients with chronic conditions or for older patients. Wearables can help patients with chronic diseases keep the hospital informed about their vital statistics while they stay at home and this means that there will be a reduced need for admitting patients or having long length-of-stays. Wearables are also huge contributors to data generation and they provide untampered and real-time data about patients that can be very useful to public and population health research and tracking.

Hospitals and healthcare organizations that are concerned with being innovative, leading their industry, and remaining relevant are finding ways to adopt this technology and other new healthcare technologies to their benefit. This is the age of technology and many technologies cut across different industries. For example, the same kind of technology can be used in the sales industry, education industry, financial industries as well as healthcare industries. This provides an opportunity for these different industries to learn from one another on how they apply these technologies.

 

The Role of Government in Healthcare Technologies

With the rapid rate at which technology is infiltrating industries, early adoption has become a way for companies to stay ahead of the curve in their industry. Technology no doubt provides an advantage and many companies are taking it a step further by seeking new technology instead of sitting and waiting for new technology to come to them. The government is also part of this. Countries and cities interested in technology advancements offer incentives and grants to companies working on innovative, applying innovative technology etc. They also offer scholarships and research grants to individuals n order to further their research into technology and to encourage more people to be innovative. For example, in the United States, students are encouraged to study careers in STEMM (Science, Technology, Engineering, Mathematics, and Medicine). They are given perks and added advantages when they choose a career path in any one of these fields. This is so as to encourage innovation and take the country to a level where it’s leading in science and technology innovations in all industries, including healthcare.

 

The Role of Big Data in Healthcare Technologies

The features and capabilities of big data and internet of things technology have so infiltrated the everyday lives that we expect these capabilities without even thinking about it. We expect to log into our phone and see the exact and correct time and weather. We expect our photos to automatically save into the cloud, we check for traffic patterns on our maps app and expect it to be accurate, we expect to be able to play the most recent music release on our phones and computers without having to worry about any of it. These enabled features have become part of our everyday lives but it wasn’t always the case. So much is commercialized and handled by massive companies like Google and Facebook that we have these advantages with little to no financial cost to us. The cost is however reflected in other ways – most prominent is in the loss of privacy. Our phones and devices can track our every movement and can even track our method of transportation. Our Google searches gathered over time provide an insight into our preferences and life choices which is then used by marketing companies for target marketing. Every keystroke, every status update, every google search is all stored up and is what constitutes big data. So yeah, Machine Learning for Healthcare Technologies is very important and useful to the healthcare industry but it makes you wonder, what are the disadvantages and in what ways will we pay for this in the future?