25 May Health Insurance Document Processing Digital Transformation

Posted at 01:05h in Blog

The health insurance industry is one busy sector, especially during the COVID-19 pandemic. There is an exchange of information for every interaction between insurers, brokers, and agents. Whether it is quoting, onboarding, underwriting, policy endorsements, or claims, there is almost always a paper document involved. Despite the steps made towards improving the industry’s technological capabilities in terms of modern policy and claims workflows, health insurance document processing has fallen behind.

Insurance companies need to find a way to convert documents in real-time to actionable data.  That’s where Intelligent Document Processing (IDP) comes in. It converts unstructured data locked in paper documents to structured data. By combining machine learning, Artificial Intelligence (AI) capabilities, and human intervention, IDP can greatly improve today’s health insurance document processing system.

Current Health Insurance Document Processing Workflow

Processing policy and claims documents is a complicated ordeal. It requires an entire infrastructure set to a manual process that is challenging to automate because of the documents' complexity. After all, it requires human-like insight to sort through all of the information.

Digitization is already being implemented in the insurance industry, but the demand for automation is soaring. Market pressures are stimulating the digital transformation, but the solutions are far from enough.

Intelligent Document Processing (IDP) is the next-generation solution for data capture. It can extract data from complex and unstructured documents, including forms required for an insurance claim. These complex documents most often require manual processing because an OCR cannot accurately process them.

Although a business infrastructure has been designed to make the process flow smoothly by integrating OCR, they are not enough to handle today’s document processing needs.

Challenges in Health Insurance Document Processing

Unstructured documents, such as policy forms, present problems for most process automation solutions because computers cannot read them.

For instance, claims processing requires a review of notes from an adjuster based on their conversations with the claimant. The adjuster’s notes are mostly handwritten, free-form, follow no standard format, and vary from one adjuster to the next. It can also include photos and reports from doctors or lawyers. In short, it contains a lot of unstructured data.

Claims processing requires poring through hundreds of pages of documents, extracting relevant information, then inputting them into a claims processing system. The information needed to be captured may include the claim number, coverage limits, policy number, date and time of loss, location, and other details.

This process is labor-intensive, time-consuming, and error-prone. To deal with these challenges, the industry uses keyword- and rule-based insurance automation solutions. Templates are used to define exactly where the data you want to capture is located in a particular document. Additionally, solutions follow a bunch of rules defining what information to extract and how it is processed.

However, not all documents are the same. In practice, the industry uses countless rules and templates to try to take into account every variation in the documents to be processed. But this rule-based approach is not enough to handle the insurance industry’s document processing needs.

Just take the adjuster’s notes as an example. Writing rules for this type of document is a huge challenge. It can also be costly, especially if you need to hire outside consultants or train the adjusters.

How Digital Transformation is Changing Health Insurance Document Processing

Rule-based approaches and inadequate OCR automation solutions are not enough to deal with health insurance companies' automation demands. IDP, on the other hand, has the capacity to automate the entire process. It can handle document complexity and variation of documents using various AI technologies and machine learning.

IDP automates data capture for insurance organizations.

Here are the ways IDP can benefit health insurance document processing:

Increased operational efficiency

Insurers process all types of documents in different formats. Some are machine printed, while others are handwritten. Some even come with signatures. The old document processing infrastructure is slow and tedious. IDP automatically processes these documents using AI services. If the AI can’t recognize something, IDP steps in to reconcile the issue manually.

Enhanced customer experience

Customers require real-time answers for their requests, which means that insurers need to process the documents and make decisions quickly, without compromising accuracy. IDP accelerates document processing, reducing the amount of processing time so you can get back to your customers at the soonest possible time.

Improved governance and compliance

Compliance with industry regulations and internal guidelines is something that insurance organizations have to deal with regularly. But insurers often have trouble maintaining auditable records because the documents are being stored in multiple systems. What sets IDP apart is its ability to deliver speed with accuracy, allowing insurers to easily comply with regulations without sacrificing their timelines.

Scalability

The IDP automated process allows you to increase or decrease capability on-demand without needing additional manpower. Whether you have a hundred or a thousand documents to process, your insurance organization can handle the workload without added cost.

Process Efficiencies

IDP improves your process efficiency by providing agility, speed, cost, time, and manual effort. In turn, you can reach decisions faster and boost your growth quicker.

Use Cases for IDP in Health Insurance Document Processing

IDP can be applied to a number of insurance use cases, including the following:

  • Claims Processing – IDP can automatically classify and annotate claims so that they can be effectively routed for evaluation and processing.
  • Appraisal Processes – Appraisal processes involve many unstructured documents, such as receipts, images, purchase and sale agreements, contractor estimates, and more. IDP can process data capture from these documents and extract relevant data, ready for analysis.
  • Commercial Underwriting Processes – Major commercial underwriting processes involve thousands of pages of documentation. The process can be greatly improved by creating underwriting criteria that can be automatically recognized and scored. IDP can also help in getting an accurate picture of the applicant’s loss history by extracting data from loss run reports.
  • Regulatory Compliance – Responding to regulatory inquiries in a timely manner requires a lot of resources and expenses for insurers. IDP can create augmented responses to these inquiries, reducing the response times and resources required for regulatory compliance.
  • Enrollment Processes – Getting new clients is always a good thing. But for -those in the insurance industry, it also means processing all the required documents. This is a largely manual process, which could mean processing up to 15 million documents per year for large insurers. Intelligent document processing technology can improve the processes involved in getting new clients by reducing the processing time. This allows them to focus their time and energy on acquiring more clients.

Starting your Digital Transformation Journey with DocDigitizer

DocDigitizer is one of the best digital transformation companies that can provide insurance companies with professional automation solutions.

DocDigitizer provides intelligent data capture services that allow insurers to manage their document processing needs. This greatly reduces the processing time of documents, allowing insurers to arrived at informed decisions based on the processed data. DocDigitizer uses a Cognitive OCR Data Capture Engine for extracting unstructured documents and Semantic Analysis powered by Machine Learning for contextual semantic information. Start your digital transformation with DocDigitizer today.