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Zahra Kohzadi , Zeinab Kohzadi , Mohammadreza Afrash, Leila Shahmoradi,
Volume 4, Issue 3 (Fall 2017)
Abstract

Introduction: If driver’s health is not controlled in the community, it might cause the death of healthy people in the best period of their lives in terms of performance, efficiency and health and it imposes a lot of financial cost on the country. The aim of this study was to Design an intelligent systems based on Fuzzy logic to determine the drivers' health condition.  
Methods: In this retrospective study, 350 health records of drivers referred to Ilam Occupational Medical Center were selected. Then, clinical information was collected from driver’s health record as a checklist and considering the criteria of accuracy, sensitivity and specificity and the area under the ROC curve, the proposed model was analyzed. Moreover, Kappa test was used for evaluation of the rate of adjustment of system results with the recorded diagnosis in the medical file. Matlab2013 was used for designing the operative rabbet.  
Results: The specificity, sensitivity and accuracy of the introduced fuzzy model were respectively 87%, 99.3% and 96.9% and kappa rate of 87% showed relatively complete adjustment of the reported rate and the area under Roc curve was calculated as 92.02.
Conclusion: According to the findings of this study, the presented fuzzy system for determining the drivers' health condition can play an important role in helping physicians and can be used in occupational medicine centers for increasing the speed and accuracy and reducing costs.
 
 

Zeinab Kohzadi, Ali Dabbagh, Mehrdad Taheri, Hassan Emami, Mahshid Ghasemi, Zahra Kohzadi, Shahabedin Rahmatizadeh,
Volume 11, Issue 4 (3-2025)
Abstract

Introduction: With the expansion of information and communication technologies, telemedicine systems have become a key tool in enhancing the quality of healthcare services. These systems enable the delivery of medical care without the need for physical presence of patients and healthcare providers, playing a significant role in the management of chronic diseases, particularly chronic pain. Effective chronic pain management requires continuous follow-up, easy access to services, and accurate information exchange between patients and the medical team. The successful design of a telemedicine system depends on the precise identification and evaluation of its functional and non-functional requirements. These requirements form the foundation for ensuring the quality, security, efficiency, and user acceptance of the system. The aim of this study is to identify these requirements to support the development of an effective system for chronic pain management.
Method: This applied, cross-sectional study was conducted in two phases. In the first phase, existing processes related to chronic pain management in relevant hospitals were analyzed. Subsequently, focused group discussion (FGD) sessions were held with 15 experts, including four medical informatics specialists, seven anesthesiologists, and four pain fellowship specialists. Participants were purposefully selected based on availability. Sessions continued until data saturation was achieved, ensuring that all dimensions of the subject had been thoroughly discussed and confirmed by the expert panel.
Results: Functional requirements were categorized into seven groups: user management (3 items), scheduling and communication (5 items), medical information management (3 items), pharmacy and e-prescription (2 items), communication and support (2 items), reporting and data analysis (2 items), and content and education (4 items). Non-functional requirements identified included reliability and availability, interoperability, security and privacy, safety, performance, and scalability.
Conclusion: The findings of this study highlight the critical role of accurately identifying and understanding both functional and non-functional requirements in the success of telemedicine systems. Optimal system performance relies on the simultaneous consideration of features such as security, reliability, interoperability, and accessibility, alongside core clinical functionalities. Therefore, the design of such systems must adopt a comprehensive and multidimensional approach to effectively meet the needs of both patients and healthcare teams.


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