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

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.
  • Machine learning: We’ve already emphasized just how big big data really is, but did you know that machine learning is one way to make big data more manageable? Health insurance companies can use this data analytics trend — which is a form of artificial intelligence — to build algorithms that automatically analyze internal and external data as it’s entered into their systems. These machine learning algorithms can be used to monitor market trends and product performance, to build predictive analytics models, to help health plan members choose the right level of coverage, and more.
  • Predictive modeling and analytics: Speaking of predictive analytics models, predictive modeling is another major big data trend taking the health insurance industry by storm. Health insurers have long used actuarial models to gauge the risks associated with insuring certain individuals and to accurately price health plans.In recent years, health insurance companies have started to turn to predictive analytics to derive insights from big data and create more sophisticated models. Rather than use these models to exclude members from certain health plan options the way they would in the past, health insurers are now using predictive modeling to align members with the right coverage for their specific needs.
  • Data privacy: Policyholders have always trusted their health plan providers to safeguard their personal medical information, but the digital age has introduced a new sense of urgency to that need for privacy due to the astronomical amount of data that is generated each day. Both federal and international agencies have established rules and regulations to accommodate the need for data privacy, such as HIPAA, which sets restrictions around how organizations must handle private health information, and GDPR, which guarantees individuals the right to be forgotten. In response to their rules and regulations, health insurers must implement enhanced data security measures, such as strong privacy policies and data encryption.
  • Value-based insurance: As part of the industry-wide shift toward member experience as a key competitive differentiator, more health insurance companies are embracing the concept of value-based insurance. In the health care industry, value-based care emphasizes the well-being of individuals and rewards physicians for proactive treatment and positive health outcomes — this represents a shift from the traditional fee-for-service model, which prioritized the number of services and procedures provided.For the health insurance industry, value-based insurance aims to improve the overall quality of health care while defraying costs. According to the National Conference of State Legislatures, with value-based insurance, “Health benefit plans can be designed to reduce barriers to maintaining and improving health. By covering preventative care, wellness visits and treatments … health plans may save money by reducing future expensive medical procedures.”
  • Unstructured data: Traditionally, health insurance providers have relied on structured data — that is, information contained within a database or a typical file format — to paint a picture of their members and their product performance. However, with the rise of social media, health insurers have been exposed to an entirely new subset of data, known as unstructured data.Unstructured data often comes in the form of multimedia, such as photos and videos, and is far more challenging to run analytics on than structured data. Fortunately, providers are finding ways to unlock the potential of this unstructured data using text analysis, sentiment analysis, machine learning, and more.

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. Armed with this information, these agents and reps gain a better 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.
  • 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.
  • Provide the right care at the right time. For many people, selecting the right health plan can be a confusing — and, at times, frustrating — process. Without proper guidance, members are liable to choose the wrong coverage relative to their health care needs. By using their company’s CRM system to apply health insurance analytics to big data, an agent can view a member’s policy and policy utilization to determine whether their current coverage is adequate relative to their needs.For example, if a member has the least comprehensive health plan available but an extremely high rate of utilization, an agent might suggest switching to a higher coverage plan. This data-based approach to member service enables health insurers to embrace the larger industry trend of value-based insurance, while simultaneously presenting new opportunities to boost their bottom line.
  • Encourage healthy habits to reduce payouts. It seems like everyone’s wearing IoT-enabled devices these days — and for health insurers, that’s great news. By collecting and analyzing data from wearable health-monitoring devices, health insurers can better gauge an individual’s health status, risks, and habits and quote rates accordingly. Insurers can also use wearables to develop custom incentive programs that encourage health plan members to engage in healthy behavior in exchange for lower premiums.
  • Fast-track claims with predictive analytics. Health plan members expect their claims to be handled quickly and efficiently but, with such a high volume of claims to sort through, this isn’t always possible for claims adjusters to achieve. With predictive analytics, however, insurers can analyze historical data — such as the claimant’s member profile and their past claims — to identify behavioral patterns and use predictive modeling to determine possible outcomes. Rather than comb through every last detail, adjusters can use this information to respond to claims faster, streamlining the entire claims process and increasing member satisfaction.

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.