Artificial Intelligence, Machine learning, Big data and Health Information: What you need to Know!

Originally Published by Data Driven Investor on Medium

Photo by Tara Winstead from Pexels

Health information and big data have been the pinnacle of a rapidly advancing healthcare technology. The progress is so fast that the healthcare community is finding it difficult to keep up creating a vacuum that encourages alternate interests and monopolies in the healthcare space.

Most of us would agree that healthcare technology is just a tool like a pen we write with or a car we drive. And we all know that we have the basic knowledge of the technology behind both tools.

But can the same be said about healthcare technology today? Probably not!

We need to understand the basics of technology to prevent its adverse consequences.

The Implications of Big Data in Healthcare

Did you know that Artificial Intelligence (AI) or Machine Learning (ML) requires a huge volume of your information in the form of big data to function? Imagine you could get paid for something that they steal from you!

Electronic medical records (EMR) generate a huge amount of data that contains information accessible easily through data mining-something which was not possible with paper records. An average tertiary care hospital generates more than 100 TB of data annually while the Library of Congress is said to have only 10 TB of text data. That means a typical healthcare institution generates more data per month than the Library of Congress!

Also, the revenue from big data and business analytics is predicted to reach $260 billion by 2022. The US is one of the biggest players contributing $88 billion in BDA revenues which account for more than 50% of the worldwide figure.

Western Europe follows the USA with revenue of $35 billion while the Asia Pacific region comes in third with $23.9 billion.

Big data is also required for the optimal performance of ML even though it uses algorithms and statistical models for functioning. Without big data, both AI and ML would become useless! One example of non-healthcare data mining is using electric bikes and scooters to map the layout and streets of a city.

We may be unaware, but every aspect of our lives from social networking to online shopping to a basic internet search, are all sources for data mining. You may or may not have consented to share the data by overlooking the fine prints in terms and conditions.

Healthcare is also vulnerable to data mining which is going on as we speak. Patient information and data generated by healthcare providers is a valuable commodity that will only become expensive with time. The data which belongs to you is being traded by large monopolized entities for billions of dollars.

Big data can be analyzed to reveal trends, patterns, and associations especially in the case of human behavior and interactions. In healthcare, the data will be made up of questionnaires, diagnostics, clinical visits, and follow-up communication.

Based on the population healthcare model big data from a hospital or entire healthcare system is mined to generate multiple data points to identify patients who need additional care.

The centralized nature and current trade practices make it unsafe while increasing the incentives for pirates to hack this information.

Health Information is Your Private Property

Giving access to your health data equates with giving access to your private and proprietary information which can be used against you or in your favor. For example, if an insurance provider gets to know you have purchased a cigar, they may increase your premium.

Large corporations are gaining full control of our healthcare system by disregarding independent physicians. They are also gaining control over your health information without your informed consent at no additional cost in a space that doesn’t have enough government oversight or control. Slowly the monopolistic nature of healthcare is emerging as we are walking towards a new era of a corrupt private totalitarian system that will not only destroy the value and quality of Hippocratic personalized medicine but also take the cookie-cutter medicine approach to the next level.

It’s about time we demand appropriate government policies and start asking the important questions. We have to give the ownership of medical big data back to individual owners via appropriate decentralization so that pirates don’t have any incentives to hack them. We need a system where you can be the owner of your own data with control over your health and healthcare. You should be the one to decide who you want to share your information with and when. We need to turn the monetary gain towards the patient, so that big corporations have to reimburse you in order to be able to use your data, instead of taking it for free.

It’s time to keep ownership of the data you worked to generate and prevent corporations from using it against your interest. The time has come to gain your individual healthcare independence!

If we don’t claim our rights, then big data will only end up empowering the wrong individuals who gain more money to lobby their interests suppressing individual patients, physicians, and healthcare providers. We will only be left to deal with misguided algorithms which don’t do any good towards machine learning.

We are the ones who can stop the broken cycle of healthcare from spiraling out of control. We need to personalize medical delivery services and preserve the personal touch in order to eliminate the corporate takeover of the medical practice.

How will it be if we could reverse the scenario and give back the power to the individual owners so that they get credited based on their health status! You get more money or credits from the insurance providers if you are more healthy and compliant with your healthcare. It will create a win-win situation for everyone and reduce costs while providing incentives to stay healthy. We can also look forward to lower premiums as well.

So what can be the incentive to keep data centralized? There’s one word for it- greed!

#AI #BigData #Deeplearning

2 views0 comments