Speed up advantages claims processing with Amazon Bedrock Knowledge Automation

In the advantages administration {industry}, claims processing is a crucial operational pillar that makes positive staff and beneficiaries obtain well timed advantages, comparable to well being, dental, or incapacity funds, whereas controlling prices and adhering to rules like HIPAA and ERISA. Companies intention to optimize the workflow—overlaying declare submission, validation, adjudication, fee, and appeals—to reinforce worker satisfaction, strengthen supplier relationships, and mitigate monetary dangers. The method contains particular steps like declare submission (via portals or paper), information validation (verifying eligibility and accuracy), adjudication (assessing protection towards plan guidelines), fee or denial (together with verify processing for reimbursements), and enchantment dealing with. Environment friendly claims processing helps aggressive advantages choices, which is essential for expertise retention and employer branding, however requires balancing velocity, accuracy, and price in a extremely regulated surroundings.

Regardless of its significance, claims processing faces important challenges in lots of organizations. Most notably, the reliance on legacy methods and handbook processes leads to frustratingly sluggish decision occasions, excessive error charges, and elevated administrative prices. Incomplete or inaccurate declare submissions—comparable to these with lacking analysis codes or eligibility mismatches—steadily result in denials and rework, creating frustration for each staff and healthcare suppliers. Moreover, fraud, waste, and abuse proceed to inflate prices, but detecting these points with out delaying legit claims stays difficult. Complicated regulatory necessities demand fixed system updates, and poor integration between methods—comparable to Human Useful resource Info Techniques (HRIS) and different downstream methods—severely limits scalability. These points drive up operational bills, erode belief in advantages applications, and overburden customer support groups, notably throughout appeals processes or peak claims intervals.

Generative AI may help handle these challenges. With Amazon Bedrock Data Automation, you may automate technology of helpful insights from unstructured multimodal content material comparable to paperwork, photographs, audio, and video. Amazon Bedrock Knowledge Automation can be utilized in advantages claims course of to automate doc processing by extracting and classifying paperwork from claims packets, coverage purposes, and supporting paperwork with industry-leading accuracy, decreasing handbook errors and accelerating decision occasions. Amazon Bedrock Knowledge Automation pure language processing capabilities interpret unstructured information, comparable to supplier notes, supporting compliance with plan guidelines and rules. By automating repetitive duties and offering insights, Amazon Bedrock Knowledge Automation helps scale back administrative burdens, improve experiences for each staff and suppliers, and assist compliance in a cheap method. Moreover, its scalable structure allows seamless integration with current methods, bettering information move throughout HRIS, claims methods, and supplier networks, and superior analytics assist detect fraud patterns to optimize price management.

On this publish, we look at the everyday profit claims processing workflow and determine the place generative AI-powered automation can ship the best affect.

Profit claims processing

When an worker or beneficiary pays out of pocket for an expense lined underneath their well being advantages, they submit a declare for reimbursement. This course of requires a number of supporting paperwork, together with physician’s prescriptions and proof of fee, which could embody verify photographs, receipts, or digital fee confirmations.

The claims processing workflow entails a number of crucial steps:

• Doc consumption and processing – The system receives and categorizes submitted documentation, together with:
  • Medical information and prescriptions
  • Proof of fee documentation
  • Supporting types and eligibility verification
• Fee verification processing – For check-based reimbursements, the system should full the next steps:
  • Extract data from verify photographs, together with the account quantity and routing quantity contained within the MICR line
  • Confirm payee and payer names towards the data offered throughout the declare submission course of
  • Verify fee quantities match the claimed bills
  • Flag discrepancies for human evaluation
• Adjudication and reimbursement – When verification is full, the system performs a number of actions:
  • Decide eligibility primarily based on plan guidelines and protection limits
  • Calculate applicable reimbursement quantities
  • Provoke fee processing via direct deposit or verify issuance
  • Present notification to the claimant relating to the standing of their reimbursement

On this publish, we stroll via a real-world state of affairs to make the complexity of this multi-step course of clearer. The next instance demonstrates how Amazon Bedrock Knowledge Automation can streamline the claims processing workflow, from preliminary submission to remaining reimbursement.

Answer overview

Let’s think about a state of affairs the place a profit plan participant seeks remedy and pays out of pocket for the physician’s charge utilizing a verify. They then purchase the drugs prescribed by the physician on the pharmacy retailer. Later, they log in to their profit supplier’s portal and submit a declare together with the picture of the verify and fee receipt for the drugs.

This answer makes use of Amazon Bedrock Knowledge Automation to automate the 2 most important and time-consuming facets of this workflow: doc consumption and fee verification processing. The next diagram illustrates the advantages claims processing structure.

The tip-to-end course of works via 4 built-in levels: ingestion, extraction, validation, and integration.

Ingestion

When a beneficiary uploads supporting paperwork (verify picture and pharmacy receipt) via the corporate’s profit claims portal, these paperwork are securely saved in an Amazon Simple Storage Service (Amazon S3) bucket, triggering the automated claims processing pipeline.

Extraction

After paperwork are ingested, the system instantly begins with clever information extraction:

1. The S3 object add triggers an AWS Lambda operate, which invokes the Amazon Bedrock Data Automation project.
2. Amazon Bedrock Knowledge Automation makes use of blueprints for file processing and extraction. Blueprints are artifacts used to configure file processing enterprise logic by specifying an inventory of discipline names for information extraction, together with their desired information codecs (string, quantity, or Boolean) and pure language context for information normalization and validation guidelines. Amazon Bedrock Knowledge Automation offers a catalog of pattern blueprints out of the field. You may create a custom blueprint in your distinctive doc sorts that aren’t predefined within the catalog. This answer makes use of two blueprints designed for various doc sorts, as proven within the following screenshot:
   1. The catalog blueprint US-Financial institution-Verify for verify processing.
   2. The customized blueprint benefit-claims-pharmacy-receipt-blueprint for pharmacy-specific receipts.

US-Financial institution-Verify is a catalog blueprint offered out of the field by Amazon Bedrock Knowledge Automation. The customized blueprint benefit-claims-pharmacy-receipt-blueprint is created utilizing an AWS CloudFormation template to deal with pharmacy receipt processing, addressing a selected doc sort that wasn’t out there in the usual blueprint catalog. The profit administrator needs to search for vendor-specific data comparable to identify, handle, and cellphone particulars for advantages claims processing. The customized blueprint schema accommodates pure language clarification of these fields, comparable to VendorName, VendorAddress, VendorPhone, and extra fields, explaining what the sphere represents, anticipated information sorts, and inference sort for every extracted discipline (defined in Creating Blueprints for Extraction), as proven within the following screenshot.

3. The 2 blueprints are added to the Amazon Bedrock Knowledge Automation mission. An Amazon Bedrock Knowledge Automation mission is a grouping of each customary and customized blueprints that you should utilize to course of various kinds of recordsdata (like paperwork, audio, and pictures) utilizing particular configuration settings, the place you may management what sort of data you need to extract from every file sort. When the mission is invoked asynchronously, it routinely applies the suitable blueprint, extracts data comparable to confidence scores and bounding field particulars for every discipline, and saves leads to a separate S3 bucket. This clever classification alleviates the necessity so that you can write complicated doc classification logic.

The next screenshot illustrates the doc classification by the usual catalog blueprint US-Financial institution-Verify.

The next screenshot exhibits the doc classification by the customized blueprint benefit-claims-pharmacy-receipt-blueprint.

Validation

With the info extracted, the system strikes to the validation and decision-making course of utilizing the enterprise guidelines particular to every doc sort.

The enterprise guidelines are documented in customary working process paperwork (AnyCompany Benefit Checks Standard Operating procedure.docx and AnyCompany Benefit Claims Standard Operating procedure.docx) and uploaded to an S3 bucket. Then the system creates a knowledge base for Amazon Bedrock with the S3 bucket because the supply, as proven within the following screenshot.

When the extracted Amazon Bedrock Knowledge Automation outcomes are saved to the configured S3 bucket, a Lambda operate is triggered routinely. Primarily based on the enterprise guidelines retrieved from the information base for the particular doc sort and the extracted Amazon Bedrock Knowledge Automation output, an Amazon Nova Lite massive langue mannequin (LLM) makes the automated approve/deny choice for claims.

The next screenshot exhibits the profit declare adjudication automated choice for US-Financial institution-Verify.

The next screenshot exhibits the profit declare adjudication automated choice for benefit-claims-pharmacy-receipt-blueprint.

Integration

The system seamlessly integrates with current enterprise processes.

When validation is full, an occasion is pushed to Amazon EventBridge, which triggers a Lambda operate for downstream integration. On this implementation, we use an Amazon DynamoDB desk and Amazon Simple Notification Service (Amazon SNS) e-mail for downstream integration. A DynamoDB desk is created as a part of the deployment stack, which is used to populate particulars together with doc classification, extracted information, and automatic choice. An e-mail notification is distributed for each verify and receipts after the ultimate choice is made by the system. The next screenshot exhibits an instance e-mail for pharmacy receipt approval.

This versatile structure helps you combine together with your current purposes via inner APIs or occasions to replace declare standing or set off further workflows when validation fails.

Lowering handbook effort via clever enterprise guidelines administration

Past automating doc processing, this answer addresses a typical operational problem: Historically, clients should write and keep code for dealing with enterprise guidelines round claims adjudication and processing. Each enterprise rule change requires improvement effort and code updates, slowing time-to-market and growing upkeep overhead.

Our method converts enterprise guidelines and customary working procedures (SOPs) into information bases utilizing Amazon Bedrock Knowledge Bases, which you should utilize for automated decision-making. This method can dramatically scale back time-to-market when enterprise guidelines change, as a result of updates might be made via information administration fairly than code deployment.

Within the following sections, we stroll you thru the steps to deploy the answer to your personal AWS account.

Conditions

To implement the answer offered on this publish, it’s essential to have the next:

This answer makes use of Python 3.13 with Boto3 1.38. or later model, and the AWS Serverless Application Model Command Line Interface (AWS SAM CLI) model 1.138.0. We assume that you’ve put in these in your native machine already. If not, confer with the next directions:

Arrange code in your native machine

To arrange the code, clone the GitHub repository. After you could have cloned the repository to your native machine, the mission folder construction will seem like the next code, as talked about within the README file:

Deploy the answer in your account

The pattern code comes with a CloudFormation template that creates mandatory sources. To deploy the answer in your account, comply with the deployment directions within the README file.

Clear up

Deploying this answer in your account will incur prices. Observe the cleanup directions within the README file to keep away from fees when you find yourself completed.

Conclusion

Advantages administration corporations can considerably improve their operations by automating claims processing utilizing the answer outlined on this publish. This strategic method instantly addresses the {industry}’s core challenges and might ship a number of key benefits:

• Enhanced processing effectivity via accelerated claims decision occasions, decreased handbook error charges, and better straight-through processing charges that decrease the irritating delays and handbook rework plaguing legacy methods
• Streamlined doc integration and fraud detection capabilities, the place including new supporting paperwork turns into seamless via new Amazon Bedrock Knowledge Automation blueprints, whereas AI-powered analytics determine suspicious patterns with out delaying legit claims, avoiding conventional months-long improvement cycles and decreasing pricey fraud, waste, and abuse
• Agile enterprise rule administration that permits fast adaptation to altering HIPAA and ERISA necessities and modification of enterprise guidelines, considerably decreasing administrative prices and time-to-market whereas bettering scalability and integration with current HRIS and claims, in the end enhancing worker satisfaction, strengthening supplier relationships, and supporting aggressive advantages choices which can be essential for expertise retention and employer branding

To get began with this answer, confer with the GitHub repo. For extra details about Amazon Bedrock Knowledge Automation, confer with Transform unstructured data into meaningful insights using Amazon Bedrock Data Automation and check out the Document Processing Using Amazon Bedrock Data Automation workshop.

Concerning the authors

Saurabh Kumar is a Senior Options Architect at AWS primarily based out of Raleigh, NC, with experience in Resilience Engineering, Chaos Engineering, and Generative AI options. He advises clients on fault-tolerance methods and generative AI-driven modernization approaches, serving to organizations construct strong architectures whereas leveraging generative AI applied sciences to drive innovation.

Kiran Lakkireddy is a Principal Options Architect at AWS with experience in Monetary Companies, Advantages Administration and HR Companies industries. Kiran offers expertise and structure steering to clients of their enterprise transformation, with a specialised concentrate on GenAI safety, compliance, and governance. He recurrently speaks to buyer safety management on GenAI safety, compliance, and governance subjects, serving to organizations navigate the complicated panorama of AI implementation whereas sustaining strong safety requirements.

Tamilmanam Sambasivam is a Options Architect and AI/ML Specialist at AWS. She helps enterprise clients to resolve their enterprise issues by recommending the correct AWS options. Her sturdy again floor in Info Know-how (24+ years of expertise) helps clients to strategize, develop and modernize their enterprise issues in AWS cloud. Within the spare time, Tamil wish to journey and gardening.