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Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and

Raheleh Maleki1, Marita Mohammadshahi2, Efat Mohamadi3,4

  • 1Ahvaz Jondishapour University of Medical Sciences , Ahvaz, Iran.

Journal of Health Organization and Management
|July 8, 2026
PubMed
Summary

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This study developed a hybrid intelligent system to accurately identify and predict insurance deductions in healthcare, improving financial management for hospitals. The system combines machine learning and expert systems to reduce financial shortages caused by claim denials.

Area of Science:

  • Healthcare Management
  • Artificial Intelligence in Medicine
  • Health Informatics

Background:

  • Healthcare systems face financial challenges, increasing reliance on insurance reimbursements.
  • Unrecognized insurance deductions lead to significant financial shortages for hospitals.
  • Efficient management of insurance deductions is critical for financial stability.

Purpose of the Study:

  • To design a hybrid intelligent system for identifying and predicting insurance deductions.
  • To combine machine learning and expert system frameworks for enhanced deduction management.
  • To address financial shortages caused by insurance claim denials.

Main Methods:

  • A mixed-methods approach involving a scoping review and expert interviews.
  • Development and testing of machine learning algorithms (e.g., CHAID) and a fuzzy expert system.
Keywords:
Artificial intelligenceExpert systemsHospital revenueInsurance deductionsMachine learning

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  • Utilized CRISP-DM methodology and validated the model using the holdout method.

    • Main Results:

    • Identified four key categories of deduction drivers: service provision, registration errors, document submission, and revenue conversion.
    • The CHAID decision tree achieved 99% precision and the lowest Mean Absolute Error (9.43).
    • A fuzzy expert system proved adaptable for classifying deductions, particularly for complex cases.

    Conclusions:

    • The hybrid model enhances the detection and prevention of insurance deductions.
    • Provides actionable insights for hospital administrators, insurers, and policymakers.
    • Optimizes revenue management and streamlines claims processing in financially constrained environments.