Preeclampsia is one of the most common complications of pregnancy that is very difficult to control and manage during the outbreak of COVID-19. One way to control and manage this disease is to use self-care applications. Therefore, the aim of this study was to design and develop a mobile-based application to facilitate self-care for women, who suffer from pregnancy poisoning in the COVID-19 pandemic. This study was conducted in two stages: In the first stage, according to the opinion of 20 obstetricians and pregnant women, a needs assessment was performed. In the second stage, based on the identified needs, the application prototype was designed and then evaluated. For evaluation, 20 pregnant women were asked to use the application for 10 days. QUIS questionnaire version 5.5 was used for evaluation. Descriptive statistics and mann-whitney test in SPSS software version 23 were used for data analysis. Out of the 66 information needs that were identified via the questionnaire, 58 were considered in designing the application. Features of the designed application were placed in 5 categories: User’s profile, lifestyle, disease prevention and control, application capabilities and user’s satisfaction. The capabilities of the application consist of introducing specialized COVID-19 medical centers, search for the location of medical centers and doctors’ offices, drug management, drug allergies, self-assessment, stress reduction and control, nutrition and diet management, sleep management, doctor’s appointment reminders, communication with other patients and physicians, application settings. Pregnant women rated the usability of the application at a good level. The designed application can reduce the anxiety and stress due to preeclampsia feel and also improve their knowledge as well as attitude towards the COVID-19 pandemic and preeclampsia.

Background and Aims: During the COVID‐19 pandemic, college students can access health‐related information on the Internet to improve preventative behaviors, but they often judge the merits of such information and create challenges in the community. The aim of this study was to investigate information‐seeking behaviors in regard to COVID‐19 among students at Kerman University of Medical Sciences (KUMS) with the help of mass and social media.Methods: The present study is a cross‐sectional study, which was conducted using an online researcher‐made questionnaire. An invitation to participate in the study was sent to 500 students at KUMS, of which 203 were selected according to the inclusion criteria and completed the questionnaire. Descriptive statistics were used to analyze the data.Results: COVID‐19 news was mostly obtained through social media platforms such as WhatsApp, Telegram, Instagram, radio, and television, as well as online publications and news agencies. Social media platforms such as WhatsApp, Telegram, Instagram, and satellite networks such as BBC contained the most rumors about COVID‐19. Some of the most common misconceptions regarding COVID‐19 were as follows: “COVID‐19 is the deadliest disease in the world,” “COVID‐19 is a biological attack,” and “COVID‐19 disappears as the air temperature rises.” In addition, most of the virtual training provided through mass media focused on “refraining from visiting holy places and crowded locations such as markets,” “observing personal hygiene and refraining from touching the eyes, nose, and mouth with infected hands,” and “the role of quarantine in reducing the incidence of COVID‐19.”Conclusion: Our findings demonstrated that during the pandemic, students used social media platforms the most to obtain health‐related information and these media have a significant impact on their willingness to engage in preventative behaviors and take the COVID‐19 risk seriously.

Background: Smart and practical health information systems and applications with fewer errors are crucial for healthcare facilities. One method that ensures the proper design of health information systems (HIS) and applications is usability evaluation. Objective: This study aimed to evaluate the usability of the emergency information systems used at the emergency departments of four educational hospitals in Kerman, Iran. Method: This study was conducted in two phases. In the first phase, the information systems' errors and shortages were identified using a semi-structured questionnaire by users (nurses and the IT staff). In the second phase, based on the results of the first phase, two questionnaires were designed for each group of users to their opinions about the usability of the emergency information systems. Results: The average score of “reducing and facilitating user’s daily activities” was significantly different among hospitals (p = 0.03). Shahid Beheshti Hospital obtained the lowest usability score (17.5), and Afzalipour Hospital received the highest usability score (21.75). Moreover, the average score in “use of the HIS” for nurses and IT staff was 2.93 and 3.54 on a scale of 5, respectively. Conclusion: Usability evaluation of health information systems is essential to ensure that these systems provide sufficient and accurate information and requirements for users and health care providers. Also, modifying health information systems based on the user views and expectations improves the quality of the system and user-system Interaction.

Background: Despite advances in patient safety, adverse transfusion reactions (ATRs) continue to occur in clinical settings and remain a primary focus of hospital hemovigilance committees. Artificial intelligence (AI) has emerged as a promising tool for detecting and preventing these complications. The objective of this study is to synthesize the evidence on the applications of AI in identifying and predicting ATRs, and to examine the existing evidence regarding the feasibility and effectiveness of employing these tools in active clinical management. Methods: This systematic review (SR) was conducted according to the PRISMA 2020 guidelines. English-language articles published within the last decade, focusing on the application of AI in the Identification and Prediction of ATRs and Implications for Clinical Management, were retrieved from the PubMed, Scopus, and Web of Science databases and subsequently analyzed. The quality of the included studies was assessed using the QUADAS-AI tool, and the findings are presented descriptively. Results: This SR showed that in the 24 included studies, AI models were primarily applied across four main focal areas: transfusion risks and outcomes, risk and moderating factors, transfusion volume and intensity, and classification and extraction of ATRs. In the included studies, the most essential model evaluation metrics were AUROC and Sensitivity, each reported in nine studies, followed by Accuracy and F1-Score, each reported in five studies. Among the studies, the Random Forest (RF) model was used more frequently than other models. Moreover, none explicitly addressed the development, implementation, or clinical evaluation of an active management system based on AI. Clinically, most studies focused on transfusion-related complications such as mortality, bleeding, and morbidity. The majority of the studies were conducted in the field of Hematology, followed by cardiology, surgery, and ICU units. Conclusions: Based on the interpretation of results, individual patient factors and transfusion volume play a pivotal role in the occurrence of ATRs. Implementing safe transfusion strategies, including the use of clinical decision support systems (CDSS) integrated with electronic health records (EHR) and personalized medicine approaches, alongside adherence to ethical considerations and patient privacy protection, is essential in future research. This study also identified two significant research gaps: first, the lack of research on the implementation or clinical evaluation of AI-based active management systems for ATRs; and second, the analysis of population groups revealed that research has been predominantly focused on adults, highlighting a gap concerning vulnerable populations, particularly pediatric patients.

Background/Objectives: The integration of artificial intelligence (AI) into primary health care (PHC) holds significant potential to enhance efficiency, equity, and clinical decision-making. However, its implementation remains uneven across contexts. This study aimed to identify the systemic, contextual, and governance-related determinants influencing AI readiness in PHC, comparing two distinct health systems, Quebec (Canada) and Iran. Methods: A qualitative, comparative design was employed. Data were collected through semi-structured interviews and focus group discussions with key informants in both settings. A framework-guided content analysis was conducted based on the four Primary Care Evaluation Tool (PCET): stewardship, financing, resource generation, and service delivery. The analysis explored shared context-specific challenges and requirements for AI implementation in PHC. Results: Analysis revealed that AI readiness is shaped more by systemic coherence rather than technological availability alone. Across both contexts, governance- and financing-related challenges were reported by the majority of participants, alongside limited data interoperability. In Quebec, challenges were more commonly articulated around operational and ethical concerns, including workflow integration, transparency, and professional trust. In contrast, participants in Iran emphasized foundational deficiencies in governance stability, financing mechanisms, and digital infrastructure as primary barriers. Across both settings, adaptive governance, sustainable investment, data standardization, and workforce capacity-building consistently emerged as key requirements for AI integration in PHC. Conclusions: AI readiness in PHC is a multidimensional process, in which implementation priorities must align with system maturity. This comparative analysis underscores that while high-resource systems must prioritize ethical integration and workflow alignment, middle-resource settings require foundational investments in governance and infrastructure. This reinforces that AI readiness is a context-dependent and phased process rather than a one-size-fits-all endeavor.