Data Analytics in Claims Processing: Tools and Benefits

Published on December 16, 2021

Health claims contain a wealth of information that can be turned into valuable insights for insurers. The potential of analytics in this domain goes beyond claims management, helping the industry reduce fraud and make healthcare payments more transparent. In addition, almost 70% of life insurers admit that predictive analysis helps them reduce expenses and improve sales. But despite this fact, data analytics in claims management is still underestimated.

As the acknowledged top software developer specializing in healthtech, Demigos knows how to get the most out of information from claims and reshape your business with software solutions. 

In this article, we will explain how to optimize health insurance claims with data analytics. We’ll also run you through modern analytical tools and solutions to help you decide what fits your business best. 

Potential information healthcare claims may include

Health claim data contains a lot of valuable information that can be used by insurers in decision making

An insurance claim is a request to an insurer to pay or reimburse a provider for medical services. The file is often completed electronically by the healthcare provider on behalf of the patient. Claims can come from physicians, facilities, and retail pharmacies. Depending on who issues the claim, its content will differ. 

In general, all claims include the following information:

  • insurance policy number

  • member identification

  • provider information (NPI, tax ID number)

  • the description of the received medical services

  • service date

  • other information related to insurance (such as dual coverage or coinsurance) 

Physicians and facility claims also contain diagnosis codes according to the International Statistical Classification of Diseases and Related Health Problems (ICD). Additionally, providers specify the location where services were provided and the facility type (for example, a physician’s office or an emergency room). 

These claims also provide information about the payment type. Physicians sum up the cost of all provided procedures such as examinations, laboratory testing, or surgery. These procedures are listed in the form of 5-digit codes according to the Healthcare Common Procedure Coding System (HCPCS).

Meanwhile, hospitals and surgical centers are usually paid for complex services like a hospital stay. The service cost already contains the cost of all provided medical and non-medical procedures, so it has another coding system: Diagnosis-Related Group (DRG). The DRG code in the claim often summarizes a range of HCPCS codes, revenue codes (high-level services such as labor room, physical therapy, and others), and the codes of more specific procedures (for example, arterial bypass).

Finally, pharmacy claims contain information about the prescribed drugs (National Drug Code (NDC) numbers) and their dosage. 

There’s no denying that claims contain a lot of valuable data. Leading insurers are already beginning to use data and analytics in claims handling for litigation and payout optimization. The use of health claim data can be connected with medical software to provide a broader view on the entire patient journey and thus help insurers prevent fraud and minimize risks. 

Let’s have a closer look at how data analytics improve claims processing.

Using data analytics to optimize the claims process

There are numerous ways of how to optimize health insurance claims with data analytics

American healthcare isn’t perfect, and people do receive ridiculous bills that are mostly covered by insurers. Statistics show that more than 91% of Americans had insurance coverage in 2020. Poor claims management and guesswork instead of tech-based medical data analytics can turn into an insurer's nightmare when a patient should be paid $100,000 for something like a broken finger. 

For example, in Seattle, a COVID-19 patient received a 180-page bill for more than $1 million after just two months of treatment. Medicare will pay the most of this amount. 

There are many ways data analytics can transform the claims process and reduce incorrect payments. Here are the brightest examples.

Read also: Importance of Data Aggregation in Healthcare

Fraud detection

As many as 78% of Americans are concerned about insurance frauds, and it’s no wonder. Health care fraud schemes cost the country at least $68 billion every year. Though the fraud plots are getting more complex, most schemes involve improper insurance claims, treatment by excluded or non-certified medical providers, medical identity theft, and cyberattacks on healthcare facilities. 

Insurers can better identify suspicious claims and avoid hefty payouts by implementing software solutions for real-time healthcare claim data analytics. Such solutions often come with built-in tools for basic fraud detection algorithms and combine predictive modeling, database search, text or data mining, visualization, and reporting tools. 

Payment recovery and subrogation

More often than not, subrogation cases are overlooked by insurers due to large volumes of unstructured claim data that contains adjuster notes and police reports along with medical records. The combination of text analysis and machine learning algorithms enables insurers to identify typical phrases and codes that indicate a potential subrogation case. As a result, insurers can detect such cases earlier, improving the recovery of payments. 

Payout optimization and settlement 

Insurers are pressured to settle claims faster and lower their costs. On the one hand, a fast-track process guarantees more transparency. But on the other hand, instant payouts of claims reported for the first time can lead to overpayment because the insurer can’t effectively predict claim size and duration.

The analysis of claim histories can help insurers identify trends and thus forecast the overall cost. This is particularly useful when dealing with long-tail claims. With data analytics in claims management, insurance companies can estimate and set the limit for instant payouts and calculate their loss reserve and the amount of money needed for future claims. 

Litigation prevention

When attorneys step in, settlement amounts rise in size. Disputed claims increase the expenses of insurance companies and take both time and effort to deal with. The best way to address such claims is to spot and assign them to the most experienced employees before the risk of a court hearing occurs. 

Data analytics in insurance claims processing allows insurers to calculate the possibility of litigation and identify those claims that will most likely end up in court. A swift response increases the chances of settling these claims faster and at a lower cost. 

Better product efficiency

Data analytics can support the industry’s slow shift toward what’s known as “claim management without claims.” Software solutions can assess the set of conditions and confirm the settlement instantly using machine learning, comparative analysis, and claim history data. 

Analytics also improves pricing and billing transparency, enabling insurers to improve ROI and patient satisfaction. The latter is particularly important, as almost a third of US adults were either underinsured or had a coverage gap at the beginning of 2020. By integrating data analytics into claims operations, insurers can decrease the number of inadequately insured patients. 

As the insurance industry evolves, patient expectations grow, and the rivalry between insurers becomes more intense. However, automated claims management with built-in tools for predictive analytics can help insurance companies stay on top. 

How to compile and analyze health claims data: Tools and solutions

Custom development enables insurers to get versatile and secure solutions for health claim data analytics

Comprehensive healthcare insurance software that covers most business needs like Cegedim and iNube becomes a natural choice for many insurers. It can automate operations, unify raw data into a searchable database, improve customer services, and complete a lot of other tasks. However, these solutions are rarely tailored to healthcare claims data analytics, enabling companies to analyze only the most common metrics such as insurance policy numbers, member plan details, and patient demographics. 

On the other hand, there are a variety of claims analytics software solutions available on the market. Yet, only a few of them can be used in healthcare due to strict regulations and integration complexity. Compliance with data privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), requires these solutions to have enhanced security measures, including data encryption and access controls.

Read also: How to Develop Health Insurance Software

The most secure option for the majority of insurance companies is custom development. A professional team of developers can address data privacy challenges and build a versatile solution that will cover fraud detection, risk assessment, payout optimization, and other functionalities.

Custom claim analytics software can offer a variety of tools that are in line with the latest trends in health insurance. Here are some of them:

  • Patient analysis. Health insurance is shifting toward member-centric plans that focus on individuals rather than a group. Claim analytics software can analyze a wide range of patient parameters, such as patient sex, age, social status, and other demographic characteristics.This will allow insurers to get more accurate results and improve their services. 

  • Unstructured data analytics. Unstructured data is harder to analyze as it needs to be extracted from the source (such as multimedia) and turned into structured data (the type you see in tables). For this, custom health claims software can use tools for text and sentiment analysis, machine learning, natural language processing (NLP), and more.

  • Predictive modeling. Risk estimation is based on predictive and “what if” modeling. Health claims analytics software can use claims data to calculate the risk factors and spot those claims that will cause additional losses or lead to court. By using predictive analytics together with machine learning, insurers can create complex insurance plans instead of simply avoiding high-risk patients.

  • AI-based tools. Using artificial intelligence technologies in health claim analytics software development allows insurers to transform analytics into data-driven insights and actions. For example, machine learning algorithms can use complex comparative analysis to identify trends and patterns as well as predict future performance or potential risks. AI-driven tools can also automate claim creation and processing, simplify patient interactions, and even encourage high-risk patients to improve their health (for example, by offering gift cards for attending cardiac rehabilitation classes).   

On top of that, advanced data analytics in claims processes requires a lot of data. Custom health claims analytics software can enable integrations with internal and external systems, which will allow it to combine health claims data with data coming from other sources. This may include CRMs, websites, client portals, healthcare providers’ EHR systems, and even social media. 

Improving claim analysis with Demigos

Healthcare claim data analytics enables insurance companies to optimize and speed up settlement cycles, recognize risks, and reduce fraud rates. With proper analytics tools, health claims data can even help insurers refine their offerings. 

The best way to receive secure and versatile software that will smoothly integrate into your existing infrastructure is to opt for custom development services from a reliable vendor. If you are about to jump aboard this train, Demigos is here to help. With extensive expertise in developing complex healthtech and agetech solutions, we know how to build the product that will take your claims management to the next level. 

Contact us to discuss your project.

Ivan Dunskiy
Ivan has been working in the tech industry for more than 10 years as a Quality Assurance Engineer, Mobile Software Developer, and Product Manager. Co-founder of 2 startups.