Ever since the Obama administration had passed the Affordable Care Act, health care and health insurance coverage have been active topics within the political climate of the United States.
Due to prior decisions made by the Nixon administration, the health insurance marketplace had been created, making health care a confusing phenomenon. Now that deep learning is being used by health insurers, the whole health insurance marketplace is now experiencing a gamechanger.
What is Deep Learning?
Before we explore deep learning, we must first understand machine learning. Machine learning is a framework of algorithms used to create a neural network within a computer model based on the biological neural network of the human brain.
Deep learning is a sub-category of machine learning. Machine learning is a form of artificial intelligence. What differentiates deep learning from machine learning is the use of unsupervised learning.
The health insurance market is one of many industries to be affected by the advent of deep learning. Deep learning software has many applications, both in the health insurance marketplace and the healthcare industry as a whole.
Unnecessary Care and Fraud
Medicare, Medicaid services, and many features of the Affordable Care Act fall within the framework of government-assisted health insurance coverage. Other health coverage would fall within the framework of private health insurance. Regardless of the framework of the health insurance plan, growing premiums can be a real problem for the average consumer within the United States.
One of the primary culprits of these growing premiums is unnecessary care. The key to preventing this unnecessary care is analyzing the data behind each patient’s situation. This involves lots of details such as prior medical history, prior medication, environmental health issues, etc. In order to aggregate and analyze this data properly, the information needs to be organized into datasets and managed with various deep learning models.
Early and accurate diagnosis is also a key in the prevention of unnecessary care and qualified professionals with solid neural network training with access to an efficient graphical user interface, GPU and CPU, are important deep learning tools for physicians that seek accurate and early diagnosis.
This need for a deep neural network and deep learning framework for diagnosis is also due to the fact that the human genome is very complex and that a lot of raw data needs to be aggregated into datasets.
The same can be said for fraud prevention. Criminals take advantage of disorganization with the systems of large insurers and the marketplace as a whole. By deploying deep neural networks, insurers can organize and monitor all of their datasets in a way that was not possible before the existence of a deep learning framework. In addition, deep learning tools like image recognition and computer vision have increased security measures in a way that allows data scientists to interpret all of the big data that is needed for a reliable, twenty-first-century security system.
Calculating the Costs and Terms of Insurance Policies
When calculating the particulars of insurance policies, the private insurer is bound by certain regulations. Insurers have a financial interest inaccuracy. Factors like age, smoking, location, and pre-existing conditions are all considered when a private insurer is preparing a health insurance plan, especially during a special enrollment period.
All of this information needs to be gathered and categorized into datasets within a neural network. Obviously, a deep learning framework is needed for that task.
Furthermore, due to the financial incentives involved, this big data needed to be interpreted by data scientists through the use of data analytics so that the private health insurance company has the right business intelligence to get a competitive advantage in the marketplace.
