{"title":"Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.","authors":"Raheleh Maleki, Marita Mohammadshahi, Efat Mohamadi, Hojjat Rahmani, Alireza Atashi, Ebrahim Jaafaripooyan, Mohammadreza Mobinizadeh","doi":"10.1108/JHOM-06-2025-0308","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks.</p><p><strong>Design/methodology/approach: </strong>A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method.</p><p><strong>Findings: </strong>Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling.</p><p><strong>Originality/value: </strong>The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.</p>","PeriodicalId":47447,"journal":{"name":"Journal of Health Organization and Management","volume":" ","pages":"1209-1225"},"PeriodicalIF":2.4000,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Health Organization and Management","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1108/JHOM-06-2025-0308","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks.
Design/methodology/approach: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method.
Findings: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling.
Originality/value: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.
期刊介绍：
■International health and international organizations ■Organisational behaviour, governance, management and leadership ■The inter-relationship of health and public sector services ■Theories and practices of management and leadership in health and related organizations ■Emotion in health care organizations ■Management education and training ■Industrial relations and human resource theory and management. As the demands on the health care industry both polarize and intensify, effective management of financial and human resources, the restructuring of organizations and the handling of market forces are increasingly important areas for the industry to address.
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