Optimizing Claim Approvals Using ICD-11 and Epic

Health insurance companies face ongoing challenges in streamlining claim approvals while minimizing errors and fraud. With the transition to ICD-11 and the widespread use of the Epic Electronic Health Record (EHR) system, business analytics professionals have a unique opportunity to enhance claim processing efficiency.

The Problem: Delayed and Denied Claims

A health insurer has noticed an increase in claim denials due to coding inconsistencies and missing clinical documentation in ICD-11 submissions via Epic. The company’s leadership asks:

🔹 Which providers and procedures have the highest claim denial rates? (Descriptive Analytics)

🔹 What coding or documentation issues contribute to denials? (Diagnostic Analytics)

🔹 Can we predict which claims are likely to be denied? (Predictive Analytics)

🔹 How can we optimize claims processing to reduce denials? (Prescriptive Analytics)

The Solution: Applying Data Analytics

✔ Descriptive Analytics – Analyzes Epic EHR and claims data to track denial rates by provider, procedure, and ICD-11 code.

✔ Diagnostic Analytics – Uses a Multinomial Logit Model to determine whether denials stem from ICD-11 coding errors, missing documentation, or policy mismatches.

✔ Predictive Analytics – Applies a Probit Regression Model to estimate the likelihood of claim denial based on ICD-11 codes, provider compliance history, and claim complexity.

✔ Prescriptive Analytics – Implements real-time Epic alerts to flag potential coding errors before submission, reducing administrative delays and improving claim acceptance rates.

Results & Impact

After integrating ICD-11 coding validation in Epic and applying predictive analytics, the insurer reduces claim denials by 18% and processing time by 30%, improving provider satisfaction and financial performance.