Irena Stojanovikj

Enhancing Billing Accuracy in

Healthcare Management


Ambulant care companies in Germany faced a 12% revenue loss due to billing inaccuracies and claim denials. Complex insurance rules around frequency limits, budget constraints, and cost bearer assignments created confusion, leading to manual errors, payment delays, and compliance risks.

Disclaimer: This case study does not include product interface visuals. The solution was primarily implemented in the background, focusing on system logic, automation, and user alerts. As a result, the user-facing interface changes were minimal and limited to occasional alert modals rather than major UI components.

  • My Role


  • Led the end-to-end billing accuracy improvement as Product Manager, managing a cross-functional team of designers, engineers, and domain experts. Conducted research with stakeholders, analyzed 150+ billing disputes, and translated complex insurance regulations into user-friendly validation systems.

Research & Discovery


User Interviews revealed care managers struggled with changing insurance regulations and felt anxious about billing errors, while our internal billing team was struggling with manual corrections.


Key Pain Points:

  • · No visibility into insurance coverage during service planning
  • · Confusion about which services were billable to which payer
  • · Billing rejections requiring awkward conversations with clients about unexpected charges, eroding trust in our platform

Solution: Three Core Features


1. Real-Time Coverage Alert System

Built automated validation that instantly alerts users when services exceed insurance coverage, offering private-pay options for overages.


2. Automatic Cost Bearer Separation

Automated service classification by payer type in documentation, eliminating manual sorting and reducing processing time.


3. Non-Billable Service Detector

Real-time notifications for non-billable services with clear separation in final documentation to prevent accidental charges.

Design Principles


· Provide real-time feedback during planning (not just at billing)

· Make complex insurance rules transparent and understandable

· Clearly separate services by payment responsibility

  • · Minimize workflow disruption

Expected Impact (Pre-Release)


Primary Target: 40% reduction in claim denial rates within 6 months


Secondary Targets:

  • · 30% decrease in claims processing time
  • · 25% increase in first-pass acceptance rate
  • · Significant reduction in manual billing corrections

  • Early testing showed successful identification of common billing errors, with billing specialists reporting potential reduction from "several hours per day" to "a much more manageable process."

Key Challenges


· Complex Regulations: German healthcare rules required multiple iterations to translate into automated logic

· User Resistance: Required change management and education for adoption

· Legacy Integration: Coordinating with systems not designed for this level of validation

  • · Balancing Compliance: Ensuring strictness for error prevention while maintaining flexibility

Key Learnings


· Early stakeholder involvement was crucial for understanding problem nuances

· Iterative implementation delivered value incrementally vs. waiting for complete solution

· User education is essential, even the best technical solution needs proper training

  • · Data-driven prioritization helped focus efforts on highest-impact validation rules

Future Vision


  • AI integration could further enhance the system by learning from past errors, predicting problems before submission, and automatically adapting to changing insurance policies, creating a self-improving billing accuracy system.