Hospital Practices and Research

Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) have transformed computer-aided drug design (CADD) by leveraging big data and advanced algorithms to accelerate drug design. These technologies enhance the exploration of chemical spaces, prediction of drug-target interactions, and development of personalized therapeutics, particularly for complex diseases like cancer.
Objectives: This review aims to evaluate the role of AI and ML in CADD, focusing on their applications in high-throughput screening (HTS), three-dimensional (3D) protein structure prediction, and drug-target identification, while addressing challenges and future prospects.
Methods: A comprehensive analysis of recent literature (2005–2024) was conducted using scientometric tools like VOSviewer to identify trends and keywords in CADD. AI-driven methods, including deep learning frameworks (e.g., TensorFlow, AlphaFold2) and computational techniques (e.g., molecular dynamics simulations), were reviewed for their contributions to drug design.
Results: AI and ML have streamlined HTS, improved 3D protein structure prediction, and enhanced drug-target identification, reducing development timelines and costs. Tools like AlphaFold 2 and QuoteTarget have identified novel drug targets with high accuracy. However, challenges such as data quality, model interpretability, and ethical concerns persist. Interdisciplinary collaboration has driven innovation in personalized therapeutics.
Conclusion: AI and ML have revolutionized CADD, offering efficient and precise solutions for drug design. Overcoming data and ethical challenges through interdisciplinary efforts and advanced algorithms will further enhance the development of targeted therapies, reshaping therapeutic paradigms for complex diseases.

Abstract Background: Human rights, which are very important for the health system and hospital accreditation requirements, include the issue of patient rights in hospitals. Artificial Intelligence (AI) is one of the advanced technologies in this industry, which aims to improve the standard of medical services for patients.
Objectives: This study was conducted with the aim of improving the enhancing patient's rights criteria in hospital accreditation standard by using AI (case study: selected hospitals in Zahedan county), which examines the factors affecting patient rights and the benefits of using AI.
Methods: According to the subject and purpose, the research approach is cross-sectional and descriptive, which was conducted in several hospitals in Zahedan in 2023. Experts in this field were given a questionnaire to complete as part of this study. Then the most important effects in this field were ranked.
Results: Experts ranked the factors of better disease diagnosis, prevention and prediction, advanced treatment methods, and easier access to medical data as the most important factors. The findings of this study indicate that if AI is used in selected hospitals of Zahedan, the rights of patients will be consistent with international validation criteria.
Conclusion: The factors covered in this study are necessary for the successful integration of AI in the health system, as well as following the guidelines that apply to any other intelligent system, including technical training and considering organizational, managerial and economic aspects. In order to intelligently adapt these systems, stakeholders in the health sector must use these components.

Abstract Background: Digital transformation through the use of technologies like blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT) is the main focus of the 4th generation technology. In smart hospitals, the 4th generation technology boosts productivity while cutting expenses, permits monitoring and early diagnosis, and enhances the quality of medical services.
Objectives: This study was carried out with the intention of implementing the 4th generation technology tools in smart hospitals, given the current technological breakthroughs and the need for smart hospitals in Iran.
Methods: The current research is bibliometric in nature. All the papers published between 2000 and 2024 that had focused on the themes of smart hospitals and the 4th generation technologies which contained keywords like blockchain, AI, the IoT, and smart hospitals make up the study's population. Advanced searches in the citation ScienceDirect and PubMed databases were used to gather the data for this investigation. The citation network was visualized and examined using VOSviewer software.
Results: According to this survey, when it comes to the use of the 4th generation technology tools in smart hospitals, 40% of the articles mention improving patient care, 35% highlight operational efficiency, and 25% stress data driven decision making as important elements.
Conclusion: In addition to boosting patient safety, monitoring, early diagnosis, and the quality of medical services, the 4th generation technology tools are essential for converting a conventional hospital into a smart hospital and cutting expenses. These instruments are therefore crucial for enhancing patients' comfort and well-being in smart hospitals.

Abstract Background: Smart hospitals today use Artificial Intelligence to improve the quality of their services. In this sense, optimizing the patient's electronic medical record is one of the most significant issues that these hospitals face.
Objectives: This study aimed to determine the role of AI in patient electronic records in a smart hospital.
Methods: This study was a systematic review, with keywords searched in PubMed, Scopus, Google Scholar, and SID databases. In Persian and English, the keywords were artificial intelligence algorithms, electronic medical records, service quality, and hospital. The inclusion criteria included publication in Persian or English, full-text papers, current publications, and a focus on the use of AI in electronic medical records. Finally, about 57 papers related to the investigation were picked.
Results: After reviewing previous related studies, it was discovered that AI can play a role in various aspects of electronic patient records, such as disease diagnosis, predicting relapse and recovery periods, improving treatment accuracy and reducing medical errors, digital care, and decision-support systems. This can result in a 20-30% improvement in resource planning, a 30% decrease in wait times, better resource use, and more accurate predictions.
Conclusion: Leveraging AI in electronic patient records is critical for maximizing benefits while minimizing hazards. Despite the limitations, AI has the potential to become a critical tool for smart hospitals in improving healthcare delivery and efficiency. Accordingly, healthcare leaders that incorporate AI algorithms into their systems can give more effective and up-to-date care to their patients.

Abstract Background: The Internet of Things (IoT) for patient care at home, which means that instead of going to the hospital and spending a lot of time and money, patients' clinical data can be viewed remotely and the data can be processed and then sent to the doctor.
Objectives: The use of any new technology by the society faces challenges. For this particular reason, before the introduction of any technology, the effective factors in its use must be investigated. In this research, the acceptance rate of the IoT for patient care at home is investigated.
Methods: This research was conducted using the grounded theory method with a case study of veterans who need home care. In order to collect information, 15 veterans and their nurses were interviewed in the statistical community of Zahedan.
Results: The findings of the present research revealed that the impact of IOT on the improvement of health services can be understood, and positive advertising about the performance of the IoT has a significant effect on its use and confidence. However, due to the unknown nature of the IoT and the security issue of users' information, there are doubts about its use.
Conclusion: In order to increase the use of IoT in order to take care of patients at home, the influencing factors of the IoT should be taken into account in order to create trust in IoT.

Abstract Background: The cost of health care is a large part of every household’s budget. On the other hand, as an economic entity, the hospital is constantly faced with different aspects of cost and revenue. So, we are dealing with conflicting objectives.
Objectives: The main purpose of the research is to help financial management in a specialty hospital. This article provides part of operational research under bi-level optimization for hospital managers to provide targeted financial planning. The method is based on the fact that the objective is to maximize the hospital income on one level, and on the other level, the objective is to reduce the patient’s payment.
Methods: The hierarchical and decentralized optimization problem is written as a bi-level model that minimizes patient costs and maximizes hospital revenues, which is an NP-Hard problem. The optimal solution to this problem is obtained using a genetic algorithm. Then, the hospital’s performance is evaluated by the Pabon Lasso diagram. It is shown that the use of this model has a significant effect on the hospital’s performance.
Results: Implementation of this model in the studied hospital shows that patient payment costs decreased and hospital income increased (reaching equilibrium point).
Conclusion: Hospital performance after model implementation was evaluated by the Pabon Lasso diagram and showed that it has an effective role in hospital performance.