Big Data & You: A Guide to Data Analytics in the Health Insurance Industry

Achieving High Performing Customer Experience in Insurance & Healthcare

Leverage connected technology to enhance insurance and healthcare customer service

Data has always been the cornerstone of the health insurance industry. In the days before the term “big data” was coined — or even before data as we currently know it existed — health insurance companies depended on mathematical models to predict outcomes and on information collected during health plan member onboarding to inform customer interactions. Data is still central, but much has changed in terms of the sheer volume of information, and how it is collected and analyzed.

Which leads us to big data and health insurance analytics. With advanced technology and such a massive volume of data at their disposal, health insurers would be crazy not to use big data analytics to their advantage — especially when it’s the key to solving one of the health insurance industry’s biggest challenges. The reality is that health insurance companies are no longer able to compete on the strength of their health plans alone; today’s customer expects total transparency and an exceptional experience at every stage of the member lifecycle. Based on this shift in the market, health insurers need provide more insightful recommendations to members based on their personal data so they can make better decisions regarding their coverage and overall health.

Health Insurance Data Analytics Trends

To maintain their edge in an increasingly competitive landscape, health insurers need to stay on top of the latest data analytics trends in the insurance industry.

• Member-centricity: One of the leading trends in the health insurance industry today is an increased focus on health plan members as individuals rather than as a collective group. The reasoning behind it is simple, really: Members no longer want to be treated as a nameless face in the crowd — they want to be seen, heard, and, most importantly, understood by their health plan providers.
• Combining internal and external information: Health insurance companies not only generate a massive volume of data internally through member engagement and sales, they also receive a large quantity of data from external sources. When this information is spread out across disparate systems, it becomes difficult to leverage it effectively — which is why analytics-enabled solutions capable of consolidating and blending data from various sources within a single system have become a vitally important trend in the health insurance industry.
• Wearable technology: The health care industry is always looking for new and innovative ways to use technology to encourage people to get healthy — and it’s a trend that health insurers can get in on, too (more on that later). Wearables, such as the Fitbit and the Apple Watch’s Health app, have made a big splash in both the health care provider and health insurer spaces for their ability to leverage the Internet of Things to collect data about the wearer’s behavioral patterns and to promote healthier habits.
• Artificial intelligence (AI): Each member of a health care plan is unique, and with the right algorithms, AI in insurance can analyze claims data through what is referred to as a Health Risk Assessment. HRAs are evaluations, surveys, etc. that compare members against an average. For example, members may be classified into high-risk areas, and in turn, may receive gift cards if they decide to quit smoking or attend heart health classes. This will benefit the member’s health, of course, but it will also benefit the plan.

Discover the Benefits of Health Insurance Data Analytics

Big data offers an untold number of benefits to health insurance companies willing to make the investment in data analytics technology:

• Deliver a personalized member experience. The health insurance industry has shifted from product-centric to member-centric, with exciting results. By using a customer relationship management (CRM) system to analyze big data, insurers can create member profiles that provide health insurance agents and representatives with a holistic view of each member. This information provides valuable customer service insights, including a deeper understanding of who a member is, what they value, what challenges they face, their lifetime value as a customer, and more — all of which allow for more personalized, member-centric service. Additionally, artificial intelligence can enable health insurers to personalize coverage to the individual using chatbot technology and on-demand insurance.
• Identify fraud before it happens. Health care fraud costs the United States anywhere between $68 billion and $230 billion a year — that’s 3%–10% of the nation’s $2.26 trillion in health care spending.

• And the health care and health insurance industries aren’t the only ones who lose out as a result of that fraud: Even a single instance of fraud can significantly increase health plan rates for members, too. Therefore, it’s in health insurers’ best interest to invest in superior fraud detection or, better yet, prevent fraud from happening in the first place.

  Claims investigators can now use predictive analytics to examine unstructured data, such as social media posts, identify potentially fraudulent behavior, and flag certain claims for review. By adding machine learning into the mix, insurers can monitor this behavior over time and create and implement new rules when fraudulent patterns emerge, thereby eliminating the guesswork from fraud detection and prevention.

  AI in insurance can also flag fraudulent claims for services not rendered. For example, a male patient submitting a claim for an obstetrician visit would be flagged for further investigation. This all circles back to the idea of “auto-adjudication,” meaning that if a claim can be automatically approved, a claim can also be automatically rejected or flagged for fraud.

  Artificial intelligence and machine learning can certainly help automate claims processing, but it’s still an ongoing process. In most cases, if a member calls an insurer with a question, there’s a timeframe for how long the insurer will take to get back to them with an answer. For example, an insurer may have 45 days to answer a claim question (this is usually because claims data can be complicated).

  Make Big Data a Part of Your Business

  Ready to harness the power of big data in health insurance? Hitachi Solutions is the perfect partner to help you do it.

  We take a multifaceted approach to helping health insurers get more out of their data with analytics-based solutions. On the people side, we bring years of industry experience to the table, having worked with countless health insurance providers to modernize their data estate and unlock the potential of big data. On the technology side, we’re able to leverage our expertise with predictive analytics and data science and our experience with the entire Microsoft software stack to empower data-driven decision-making with custom Dynamics 365 solutions, Data Lake, and Data Bricks.

  Don’t waste another moment; make big data analytics a part of your business today. Our specialists are ready and waiting, so get in touch today to get started.