Learn how big data analytics helps healthcare insurers prevent insurance fraud and find the right coverage for a customer.
Statista states that the global big data market is expected to grow to 103 billion USD by 2027. “Big data” describes data that cannot be analyzed without specialized software. To process complex and large-scale data sets and find trends, patterns, and insights from them, healthcare organizations use big data analytics.
What Are the Types of Big Data Analytics?
There are four types of big data analytics: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics allows an insurance company to identify patterns and trends in historical data, while diagnostic analytics explains why such trends and patterns occurred. For example, an insurance company implemented a big data analytics solution that immediately analyzed historical data on claims payments. Descriptive data analytics identified a long-standing period in which claims payments in the low-risk segment increased, and diagnostic analytics found the cause.
An insurer made a mistake in risk assessment before quoting. As a result, a certain number of clients with a family history of diabetes were classified as low-risk and low premium, although they should have been classified as at-risk and high premium. Heredity eventually emerged: the insured clients were diagnosed with early-stage diabetes. As a result, the insurance company suffered losses because the insurance plan was selected incorrectly.
Descriptive (what happened) and diagnostic (why it happened) analytics is traditional. However, now predictive (what will happen in the future) and prescriptive (what actions can change the result) analytics are in great demand as tools for risk prevention in healthcare insurance.
Why Do Insurance Companies Need Big Data Analytics?
Identify Patients at Risk of Cancellation
Predictive analytics looks at how often a patient misses in-person visits. Dmitry Baraishuk, the Chief Innovation Officer of the software development company Belitsoft with 20 years of HealthTech expertise, says that healthcare organizations use dashboards for patient no-show data analysis and prediction with run charts. These run charts provide information about attendance trends and fluctuations over time.
If the criterion of missed visits is high, most likely a patient will cancel their insurance. An insurance company can use data from a big data predictive analytics report to plan a targeted intervention, which means they can contact that patient and ask what they can do to help them. For example, if an insurance company reminds patients about in-person visits, they will be more likely not to miss them. This insurance company strategy will help increase patient satisfaction and minimize customer churn, reducing the risk of cancellation.
Assess Insurance Risks
The case about the mistake in risk assessment before quoting would not have occurred if the insurance company had used predictive modeling for underwriting. In the insurance industry, underwriting is the assessment of risk when providing coverage to a potential customer. Big data analytics analyzes the patient’s lifestyle and medical history, their family medical history, and estimates the likelihood that the patient will develop a chronic disease. If the probability is high, the insurance company sets a certain premium for such a patient. The higher the risk of developing chronic diseases, the higher the premium this patient pays to the insurance company.
Focus on Personalized Customer Experience
When big data analytics gets access to a customer relationship management (CRM) system, insurance agents can create customer profiles based on the reports received. Via the created profiles, healthcare insurance representatives will get a holistic view of the client’s personality, what they value, what problems they face, their level of satisfaction with the insurance product, and their expectations. Insurance companies can provide patients with personalized experiences based on customer profile data.
A special case of personalized customer service is when an insurance agent helps a patient choose an appropriate health insurance plan. When patients choose a health plan on their own, they may find the process to be complex. As a result, they may choose the wrong coverage relative to their healthcare needs. Analytics can use data from a CRM system and offer the insurance agent a report that describes whether the patient’s current insurance plan meets their needs. For example, analytics show that a patient has chosen the least comprehensive insurance coverage but has a high utilization rate. Based on this data, analytics can provide the insurance agent with options for plans with higher coverage most likely to meet the client’s needs. In this way, the insurance company satisfies the real client’s needs and increases its profits.
Detect Insurance Fraud Cases
Healthcare fraud is considered the most financially burdensome form of insurance fraud in the US. It results in $105 billion in insurance losses each year. For example, a healthcare provider may send a claim for a service not provided to the patient, or perform upcoding (where the provider files a claim for a more complex service than was completed). An example of patient fraud is when a patient pretends to be an insurance policyholder but is not.
Big data analytics can identify suspicious claims and provide a report to the insurance company for further investigation. For example, analytics show that a particular hospital sees a surge in claims for knee surgery from a specific doctor. An investigation based on the analytics report may show that this doctor performs surgeries more often than required, and some patients would need only physiotherapy.
Develop New Insurance Products
A competitive advantage of an insurance company is new insurance services and products for customers. Algorithm-based predictive analytics can identify areas of high demand in the insurance market and find gaps in the product mix that patients want. An insurance company can prepare innovative wellness and chronic disease treatment programs and develop new insurance products. This will provide improved access to healthcare for demographic groups with varying levels of health status.
How Can Insurance Companies Store Data for Big Data Analytics?
Because local storage cannot always cope with the volume of data for big data analysts, more organizations have turned to cloud storage. This type of storage makes data available anytime and anywhere. It has given rise to the analytics-as-a-service (AaaS) model. With AaaS services, healthcare organizations can purchase a subscription to the cloud service and perform analytics through it. It allows healthcare organizations not to spend a lot of money on on-site storage and processing, and save on the technical support needed for an on-site server network.
Of course, any cloud storage must comply with HIPAA requirements. If a healthcare provider wants assurance that a cloud storage provider is HIPAA compliant, they should enter into a business associate agreement (BAA).
In Conclusion
In a 2023 survey, the majority of respondents confirmed that data and analytics had a good impact on their companies’ productivity and profits. Big data analytics (via its predictive function) allows insurance companies to get more out of their data: increase customer loyalty by focusing on their personalized experience, and minimize insurance fraud.
