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Challenges in Auto Adjudication of Health Claims

Updated: Jun 12


Auto adjudication of health insurance claims in India holds promise for efficiency and faster payouts. However, challenges like data quality, lack of standardisation, and potential for bias hinder its smooth implementation. The IRDAI is taking steps to address these issues, but insurers and healthcare providers need to collaborate on data quality and coding practices. Transparency and explainability in claim decisions are also crucial for building trust in the system.


The Indian health insurance industry has witnessed significant growth in recent years. With rising healthcare costs and growing awareness, more people are opting for health insurance coverage. For the period from December 2022 to December 2023, Health Insurance has underwritten GDP of Rs 78,628 Cr with a growth rate of 21.37% as compared to GDP of Rs 64,785 Cr. The entire Non-Life segment has a GDP or Gross Written Premium (GWP) exceeding ₹2.1 lakh crore (₹2,10,000 crore) in FY 2022-23 [Source: Insurance Regulatory and Development Authority of India (IRDAI)]. To streamline the claims process and ensure faster payouts to policyholders, the Insurance Regulatory and Development Authority of India (IRDAI) has been pushing for the adoption of auto adjudication for health insurance claims.


What is Auto Adjudication?

Auto adjudication refers to the automated processing and settlement of insurance claims using pre-defined rules, algorithms, and machine learning models. This technology has the potential to revolutionize the claims process in India by:


  • Reducing manual workload and processing times.

  • Eliminating human error and inconsistencies in claim decisions.

  • Enabling faster claim settlement, improving customer satisfaction.

  • Identifying potential fraud patterns more effectively.


However, implementing a robust and reliable auto adjudication system in the Indian health insurance sector faces several challenges:


1. Data Quality and Standardization:


  • Incomplete and Inaccurate Data: Medical records and claim forms often contain missing or inaccurate information. This can lead to misinterpretation and errors during automated processing. The regulator is pushing for API message formats like FHIR to reduce problems due to incomplete and inaccurate data.


  • Lack of Standardization: Coding practices for medical procedures and diagnoses vary across hospitals and clinics. This inconsistency makes it difficult for algorithms to accurately assess claim validity. The regulator is pushing for the adoption of SNOMED and ICD-10 codes which will improve standardisation.


2. Regulatory and Compliance Issues:


  • Evolving Regulations: The IRDAI is actively developing regulations for auto adjudication. However, insurers need clarity on compliance requirements and data privacy concerns.


  • Explainability of Decisions: In case of claim denials, policyholders need clear explanations for the automated decisions. This transparency builds trust in the system.


3. Bias and Fairness:


  • Algorithmic Bias: AI models trained on historical data might perpetuate existing biases in claim decisions. This could disadvantage certain patient groups or treatment modalities.


  • Human Oversight: While automation offers efficiency, human oversight remains crucial to ensure fairness and address complex cases requiring medical expertise.


Recent Actions by IRDAI:

The IRDAI recognizes the potential of auto adjudication and is actively working towards its implementation:


  • Focus on Standardization: The IRDAI is emphasizing the importance of data quality in medical records and claim forms. Standardization of coding practices and data formats like FHIR, ICD-10, SNOMED are key focus areas.


  • Promoting NHCX: National Health Claims Exchange is a digital health claims platform developed by the National Health Authority (NHA) in collaboration with the Insurance Regulatory and Development Authority of India (IRDAI)


  • Draft Guidelines on Claim Settlement Processes: These guidelines outline the framework for auto adjudication, highlighting data standardization, claim processing timelines, and grievance redressal mechanisms.


Moving Forward:

Despite the challenges, auto adjudication holds immense promise for the Indian health insurance industry. Here are some key steps to ensure its successful implementation:


  • Improved Data Quality: Insurers and healthcare providers need to collaborate to improve data quality in medical records and claims submissions. Standardisation of coding practices and data formats is crucial. The implementation of NHCX along with standards like FHIR, ICD-10, SNOMED, LOINC etc will ensure standardisation of  electronic health records (EHR). Drona Pay has pre existing libraries to process standardised EHR data to automate claims processing.  

  • Investment in Analytics: Insurers need to invest in advanced analytics and machine learning capabilities to develop robust claim processing systems. There is a need to develop strong profiles for various treatments and diagnosis to identify outliers and anomalies which need adjuster / expert review. Drona Pay offers a new age Decisioning Platform to help automate claims processing. 


  • Transparency and Explainability: The claims adjudication process needs to be transparent, with clear explanations provided for claim decisions, especially denials. This builds trust in the system. Explainability and usage of Profiling and Rules is a key building block of the Drona Pay platform. 


  • Human-in-the-Loop Approach: A human-in-the-loop approach is crucial. While automation handles routine claims efficiently, human expertise remains essential for complex cases and medical judgement. Drona Pay offers a case management and BPMN modeller to support review by Adjudicators, Medical Professionals, Surveyors and Risk Analysts along with facilitating communication with Hospitals and Patients. 

Continuous Monitoring and Improvement: Continuously monitor the performance of auto adjudication systems and refine algorithms to address emerging trends and prevent bias. Back testing and Simulation are key features of the Drona Pay platform which helps insurers test and monitor the platform.

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