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Abstract-Effective hospital management involves such activities as monitoring the flow of medication, controlling treatment, and billing for the patient's treatment. A major challenge between insurance companies and hospitals lies in the way medical treatment expenses for insured patients are reimbursed. In some cases, the insurance deduction leads to the loss of revenues by hospitals. This paper proposes a framework for the handling insurance deduction that integrates three major methodologies: Decision Support Systems, Data Mining, and Multiple Criteria Decision Making. To exemplify the practical utility of the framework, it is used to study hospital services and insurance deductions are extracted from 200,000 documents in 150 hospitals in Iran. To classify the kinds of services, decision trees are developed to mine hidden rules in the data which are then modified on the basis of some performance measures. The rules are then extracted and ranked using the TOPSIS method. The results show that the proposed framework is capable of effectively providing objective and comprehensive assessments of insurance deductions.

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Bankruptcy prediction is an important problem facing financial decision support for stakeholders of firms, including auditors, managers, shareholders, debt-holders, and potential investors, as well as academic researchers. Popular discourse on financial distress forecasting focuses on developing the discrete models to improve the prediction. The aim of this paper is to develop a novel hybrid financial distress model based on combining various statistical and machine learning methods. Then multiple attribute decision making method is exploited to choose the optimized model from the implemented ones. Proposed approaches have also been applied in Iranian companies that performed previous models and it can be consolidated with the help of the hybrid approach.

Small- and medium-sized enterprises (SMEs) require less formalized project management methods than large corporations. However, project management can play a significant role in implementing innovations. Even though lean-agile project management offices (LAPMO) are becoming increasingly important for SMEs, each company’s performance varies significantly due to varying innovative capabilities and the dynamism of internal and external contexts. Based on a literature study, innovative capabilities, and LAPMO, we have developed a theoretical model with 11 assumption models. As a follow-up, we conducted empirical research, including critical variable metrics, data collection and analyses, validity tests, reliability tests, regression analysis, and structural equation modeling. The model developed in this study considers the many roles that innovation capacity and project agility play in enhancing corporate performance. LAPMO mediates the relationship between innovation and performance in small and medium-sized businesses. Organizational innovation, open innovation, and innovation capabilities affect companies’ performance. In small and medium businesses, they also affect LAPMO. For small and medium-sized businesses, LAPMO mediates the relationship between organizational innovation, open innovation, and innovation capabilities.