AI Claims Solution

Challenge and solution
 

Claims management is a central function for health insurance providers, involving the systematic processing of medical claims – from receipt and validation to reimbursement. The primary objective for payers is to ensure timely and accurate payments to providers while identifying and mitigating risks including fraud, and overuse of services.


However, the process presents significant operational and regulatory complexities. Insurers must manage high claim volumes, comply with evolving standard medical procedures, and adhere to stringent regulatory requirements – all while minimizing errors and optimizing processing times. Additionally, rising healthcare costs and the increasing sophistication of fraudulent activities necessitate more advanced tools to maintain operational efficiency.
 

To address these challenges, AI Claims solution of openIMIS provides a data-driven approach using the AI Claims Adjudication module, which applies machine learning to detect anomalies, prioritize claims for review, and streamline the adjudication process. This solution enhances accuracy, reduces processing times, and strengthens fraud prevention, enabling insurers to improve both efficiency and service quality.

 

Solution close-up


The AI Claims solution, automates the sorting, categorization, and anomaly detection of health insurance claims. The solution, utilizing various modules within openIMIS currently provides three adjudication methods, each of which can be selected individually or combined to align with an insurer’s operational workflows:

  • Rule-based method: Claims are automatically verified using predefined rules configured in the Configurable Claims Review Engine.
  • Artificial Intelligence-powered method (AI module): Claims are categorized and assessed using a machine learning-based decision support model.
  • Manual review method: Claims undergo expert evaluation by medical reviewers within the payer organization.

These methods are designed to function either independently or in tandem, offering flexibility to adapt to diverse business needs. Insurance operators have the ability to decide to what extent to automate claims management.


Example workflow for an Integrated Adjudication Process:

 

  1. Claims are initially screened by the rule-based engine according to user-defined criteria (rules).
  2. Rejected claims are returned  to the healthcare facility (provider) for correction and re-submission, while approved claims proceed to the AI module.
  3. The AI module then determines the next steps:
    1. Direct approval (and payment) for low-risk claims
    2. Manual Quality Assurance (QA) for claims requiring additional validation to ensure the accuracy of the AI’s assessment. Following manual review, claims are either approved for payment or returned for correction and re-submission. 
       
Adjudication Process

 

Benefits for all parties involved
 

  • For payers (operators): Reduces time and resource expenditure by minimizing need for manual claim reviews.
  • For healthcare providers: Ensures a fair and efficient claims assessment process, and faster reimbursements.
  • For decision-makers (policy level): Efficient and accurate insurance claims provide policy makers opportunities to increase/improve benefit packages.

Interoperability and Customizability
 

The AI Claims Solution is designed for seamless integration within openIMIS workflows but can also operate as a standalone system, i.e. integrate with any insurance management platforms. By adhering to HL7 FHIR standards, the solution ensures compatibility, enabling it to receive and process claims from any system – both for training and operational purposes.
 

The openIMIS community of practice fosters ongoing collaboration, providing support for the implementation and continuous development of various openIMIS software components, including the AI module. 
 

Interested? For further details, explore the module’s technical documentation and development insights on the openIMIS wiki, or reach out to the service desk at [email protected].


Reference implementation(s)


Nepal: The Health Insurance Board (HIB) uses openIMIS to manage the national Social Health Insurance (with more than 9 million beneficiaries) and is integrating an openIMIS AI claims adjudication module to assist in processing more than 80,000 claims per day. 

 

Learning resources
 

Learning Exchanges and e-Learning course on AI in Health Insurance, in collaboration between Amref Health Africa, the Asian Development Bank and GIZ