Health insurance is a source of confusion, frustration and stress for many Americans. While the federal and state governments have taken measures to improve the health insurance system, many Americans still groan at the complexities and shortcomings that leave some 15% of adults ages 19-34 uninsured, and both uninsured and insured people say insurance is too expensive.
Reforms to the nation’s healthcare system are also insufficient for many. About 11% of uninsured people had income below the poverty level but were ineligible for Medicaid because their state did not expand the program. Even reforms to the health insurance system are not reaching most of those who still lack insurance. Two-thirds of uninsured adults had not gone to the ACA Marketplace to look at options, and about one-third of them said that was because they didn’t think they could afford health insurance.
In the past decade, health insurance companies have been looking to artificial intelligence (AI) and machine learning to identify at-risk individuals and reduce rising costs in the healthcare sphere. Although it may bring up connotations of robotic systems and overwhelming technology, AI can be simply understood as a system that seems smart, and machine learning as a way the system becomes smart — by taking in enormous amounts of information and improving its own way of understanding the data.
For a system that helps people buy health insurance, that data can include hospital usage, age of children, financial risk tolerance, level of security and many other factors. While machine learning artificial intelligence may be seen as a data-hungry machine, the crucial aspect of a successful AI system that manages a client’s healthcare is its ability to develop efficient reasoning and intuitively read and understand trends.
Through my experience in health insurance sales and customer support, I’ve developed a deep understanding of the industry’s systems. I am currently the CEO of an insurance discovery platform that uses AI to better understand customer needs. Other companies like Certifi and Prognos Health also use AI to streamline processes, track data and improve the system.
As more and more healthcare and medical companies are witnessing the value of AI and machine learning within their varied systems, industry leaders are realizing that machine learning applications can potentially improve the accuracy of treatment protocols and health outcomes.
Information gathering and processing systems are being updated and streamlined for efficacy. Machine learning can be applied in healthcare through lowering the cost and chaos of recordkeeping, including electronic health records, and maintaining data integrity. The technology also offers potential for quick disease identification and higher quality in medical imaging, which can result in faster diagnoses and improved patient outcomes.
Unique And Relevant User Experiences
Applying technological advances allows hundreds of healthcare plans to be held in a single, fluid system. As the AI better understands the data and behavior of its current customers within its software, it can constantly grow and evolve. The key is what this means for the person seeking healthcare.
A system that can look at millions of data points and generate smart insights — without direction from a human agent — can be instantly scalable and create a robust user experience. The result is that consumers receive a web shop experience that caters to each user’s unique preferences because the platform has learned from each individual customer. The result is also an immense channel of communication with concierge-level service, guiding each consumer every step of the way with custom recommendations based on their specific needs and budget.
The ultimate goal is the best possible user experience, based on intuition drawn from relevant information and that weeds out the irrelevant.
Better Outcomes
It’s important to note that a majority of the price consumers pay when enrolling in health insurance goes into risk prediction and risk management. By using AI to create a system that can create more accurate risk models and predict which individuals need specific types of care, health insurance providers can spend more money on their beneficiaries and less on those processes. Platforms that can take in, study and learn from data, refine judgments and generate intelligent insight can also greatly reduce the need for expensive human data analysts.
Challenges
It’s also important to mention the challenges that come with this technology. AI uses data to generate insights. It is difficult to use AI in the insurance purchasing process because access to data to generate these insights is hard to come by. To sell someone insurance, you need to know the details of their life, health, family, scheduled surgeries, etc., to inform the best recommendation for insurance and budget. On the phone you can talk through these needs, but a machine can’t access that information from a consumer directly. There will always be tension between asking questions, slowing down the process and crunching the numbers versus getting that data from other sources and piecing it together on the consumer’s behalf.
A lot of companies are still trying to figure out how to organize data in a way that can be consumed; this is a challenge right now as we are still in early adoption. How can consumers trust that a machine made the right choice for them? How can it save time and narrow down the best options — giving consumers the choice at the end of the day?
Ultimately, I think AI-driven health insurance can deliver better outcomes and may also be able to address some of the longstanding complexities of the U.S. healthcare system. When faced with a rise in expectation and complex healthcare standards, consumers and providers can become overwhelmed in an industry that often lacks simplicity. Through the use of technology and machine learning, buying healthcare no longer has to be undermined through expenses and frustration. With better understanding of each consumer’s problems, the root of their problems and their unique healthcare needs, AI and machine learning can provide a guiding, innovative solution that simplifies the industry, organizes data and ultimately helps improve patient safety and outcomes.
Source: forbes.com
