Are we really meant to know the future? Prediction can be a varied philosophical question but predictive analytics is reality, especially when health care is part of that reality. This, of course, begs the question, “What is predictive analytics?”
Predictive Analytics Defined
Predictive Analytics is the science of creating a predictive model based on sciences related to statistical analysis, such as machine learning and data science. The basic idea is that you use the information to forecast a future event. In scientific terms, this information is data.
First, big data and historical data needs to be gathered and arranged into datasets. When the datasets get really massive and complex, algorithms are needed to create and manage these datasets. That is why machine learning and its use of neural networks are part of predictive analytics.
Second, this data needs to be interpreted and translated into digestible information in the form of business intelligence and/or data virtualization. Data scientists are needed to for this type of interpretation and useful insights.
Cost of Care
Health insurers employ these predictive analytics tools in order to create statistical models. They need these statistical models so they can create predictive models in order to calculate administrative costs and pocket costs. Private health insurers need to be able account for a private health insurance comparison within the marketplace.
Their premiums have to be based on estimates taken from statistical analysis: Health status, behavior, current data, etc. Through the use of this predictive analytics process, they can create a predictive model for future events and estimate eligibility, plan type, credit risk, quality of care, health care costs, administrative costs, pocket costs, and pocket payments.
The insurance company in question has a financial interest in the use of predictive analytics to create accurate descriptive models and predictive models. After the Affordable Care Act, private insurers no longer had the luxury of denying customer service for the creation of a health insurance policy due to pre-existing conditions. The coverage of dependents on the policy had been extended as well. While this brought new challenges, it also brought new opportunities through the individual mandate, removing the option of being uninsured unless one was below the federal poverty level.
Such increased enrollment within private coverage combined with increased requirements and regulations, demonstrated the utility of predictive analytics uses. Because business users were now required to purchase more health insurance than ever for their employees, the administrative costs of their business processes were increased.
Private insurers knew that they could use deep learning technology and data analytics to make a significant difference in their advantage over their competitors.
The Affordable Care Act was a gamechanger for public insurance in that of low-income individuals below the federal poverty level who could not previously afford insurance coverage were given basically free health insurance. This was a great victory for the ideal of universal coverage in the nobody within the United States need be without insurance coverage.
The relevance of predictive analytics to the Affordable Care Act is that with millions of Americans gaining health insurance for the first time, the was more big data than ever being added to mathematical models for both private insurance and public insurance.
With such a large amount of new people being added to the marketplace, insurers needed predictive models for future outcomes in order to make the necessary calculations for the various elements of an insurance policy: Pharmaceuticals, prescription drugs, financial services, outpatient services, inpatient services, hospitals, administrative costs, pocket costs, pocket payments, enrollment, deductibles, coverage for dependents, credit score, risk pool, and total costs.
This seminal event in public insurance also affected Medicare, Medicaid, Medicaid services, and federal government subsidies. Fortunately, the data modeling employed by the science of predictive analytics has enabled the federal government to provide some type of universal coverage to low-income individuals