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Factors Affecting the Acceptance and Use of Hospital Intelligent Management (HIM) System based on the Unified Theory of Acceptance and Use of Technology (UTAUT)
Pezhman Sadeghi, Nader Jahanmehr, Reza Rabiei,
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Information systems serve different purposes in organizational and management hierarchies. The hospital intelligent management system is an analytical and decision-support management information system that provides information and important performance indicators for managers in hospitals. Considering the role of this system in increasing the efficiency and effectiveness and the lack of academic hospitals having the desired level of productivity, this research was conducted to investigate the effective factors in improving the acceptance of the intelligent hospital management system in the hospitals of Shahid Beheshti University of Medical Sciences (SBMU). 
Materials and Methods: This descriptive and correlational research was conducted in 19 hospitals (12 teaching hospitals and 7 non-teaching hospitals) of Shahid Beheshti University of Medical Sciences in 2022. In this study, 126 senior and middle managers and experts of the productivity committee participated. The data of this study were collected Using the Unified Theory of Acceptance and Use of Technology(UTAUT)  Questionnaire and for statistical analysis, SPSS software (statistical table and linear and multiple regression tests, sequencing, and chi-square) was used. The validity of the questionnaire was determined using the opinion of research experts and its reliability was also determined using Cronbach’s alpha coefficient (0.824).
Results: Most of the participants in the study were from teaching hospitals (63.2%) and were middle managers (50.8%). Behavioral intentions were identified as the most important factors in the use of system by senior and middle managers and experts of productivity committee (P<0.001). The effort expectancy had the greatest impact on the intention to use the system as compared to the expected components of Performance expectancy and social influence. Also, training and having educational programs on how to use the HIM and its applications can increase the intention and use of the HIM by employees (P<0.001).
Conclusion: Based on the results, the effect of the moderating variables in this study was insignificant. If senior managers and influential people encourage working with the system, and employees also make more effort to learn the system, and working with the system meets their expectations, employees will be willing to use the system. In other words, employees use the system when they believe that this system is user-friendly, valuable, and useful for them.

Case-Based Reasoning: An Applied Technique for Designing Intelligent System for the Differential Diagnosis of Oral Diseases
Reza Safdari, Arash Mansourian, Shahram Tahmasebian, Niloofar Mohammadzadeh, Hamideh Ehtesham,
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: Artificial intelligence-based systems can facilitate data management and interpretation in various dental specialties and can be used as auxiliary tools in diagnosis and education. Case-based reasoning is a promising artificial intelligence method for implementing decision support systems in medical sciences. In the current research, this technique has been used to design an intelligent system for the differential diagnosis of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.

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