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Background Today, the use of data in administrative and clinical processes is quite challenging due to the large volume of data, data collection from various sources, and lack of data structure. As a data management tool, dashboards play an important role in timely visual display of critical information on key performances. Objectives This systematic review aimed to identify functional and non-functional requirements, as well as challenges of using dashboards in hospitals. Methods In this systematic review, four databases, including the Web of Science, PubMed, EMBASE, and Scopus, were searched to find relevant articles from 2000 until May 30, 2020. The final search was conducted on May 30, 2020. Data collection was performed using a data extraction form and reviewing the content of relevant studies on the potentials and challenges of dashboard implementation. Results Fifty-four out of 1254 retrieved articles were selected for this study based on the inclusion and exclusion criteria. The functional requirements for dashboards included reporting, reminders, customization, tracking, alert creation, and assessment of performance indicators. On the other hand, the non-functional requirements included the dashboard speed, security, ease of use, installation on different devices (e.g., PCs and laptops), integration with other systems, web-based design, inclusion of a data warehouse, being up-to-data, and use of data visualization elements based on the user’s needs. Moreover, the identified challenges were categorized into four groups: data sources, dashboard content, dashboard design, implementation, and integration in other systems at the hospital level. Conclusion Dashboards, by providing information in an appropriate manner, can lead to the proper use of information by users. In order for a dashboard to be effective in clinical and managerial processes, particular attention must be paid to its capabilities, and the challenges of its implementation need to be addressed.

Background Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. Methodology This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. Results Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. Conclusion Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall survival (OS), recurrence‐free survival (RFS), progression‐free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy. Recent Findings A thorough search of four major databases—PubMed, Scopus, Web of Science, and Cochrane—resulted in 2400 articles published within the last decade, with 32 studies meeting the inclusion criteria. Notably, most publications emerged after 2021. Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. Evaluation metrics such as area under the curve (AUC) (18 studies), concordance index (C‐index) (11 studies), and accuracy (11 studies) were frequently employed. Age at diagnosis, tumor stage, CA‐125 levels, and treatment‐related factors were consistently highlighted as significant predictors, emphasizing their relevance in OC prognosis. Conclusion ML models demonstrate considerable potential for predicting OC survival outcomes; however, challenges persist regarding model accuracy and interpretability. Incorporating diverse data types—such as clinical, imaging, and molecular datasets—holds promise for enhancing predictive capabilities. Future advancements will depend on integrating heterogeneous data sources with multimodal ML approaches, which are crucial for improving prognostic precision in OC.

COVID-19 has created major health-related, economic, and social challenges in societies, and its high contagion has dramatically altered access to healthcare. COVID-19 management can be improved by the use of telehealth. This study aimed to examine different telehealth technologies in the management of COVID-19 disease in the domains of surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up and investigate the challenges to the application of telehealth in COVID-19 management. This scoping review was conducted based on Arksey and O’Malley's framework. Searches were performed in Web of Science, PubMed, and Scopus databases to examine the evidence on the effectiveness of telehealth in COVID-19 management. Eventually, 36 articles were selected based on the inclusion criteria. The majority of these studies (33%) were conducted in China. Most services offered via telehealth focused on surveillance, tracking, and follow-up, in that order. Moreover, the most frequently used technologies were social networks, web-based apps, and mobile apps, respectively. The use of telehealth in COVID-19 disease management plays a key role in surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up.

Hypertension is a chronic condition, and a major risk factor for other chronic conditions requires management. Considering the growth and extensive use of mobile health (mHealth) technologies and their capabilities, it is essential to examine the effects of these technologies on hypertension control and self-management. The present systematic review examined the effect of using mHealth technologies in controlling blood pressure and investigated the functionalities of mHealth technology on self-management aspects of patients with hypertension. A systematic search was conducted on PubMed, Web of Science, Embase, and Scopus databases. Clinical trials in English investigating the use of mHealth technologies for blood pressure control published from 2005 to 2018 were included in this study. The functionalities of these technologies were also investigated. These functionalities were divided into five categories of monitoring, alarms, feedbacks, education, and communication. The most frequently used technology for hypertension control was smartphones in the 15 articles examined. Moreover, the most frequent functionalities used for self-management of hypertension were communications and reminders, education, monitoring, and feedback, respectively. In the majority of the studies, these functionalities were employed in combination with mHealth technologies, a feature that affects hypertension control and self-management. The use of mHealth technologies, such as smartphones, positively affects hypertension self-management and reduces blood pressure. Functionalities such as communication and reminders, education, monitoring, and feedback are effective in hypertension self-management programs. The simultaneous use of these functionalities combined will be more effective in hypertension self-management programs.

Background and Aims Considering the rapid spread and transmission of COVID‐19 and its high mortality rate, self‐care practices are of special importance during this pandemic to prevent and control the spread of the virus. In this regard, electronic health systems can play a major role in improving self‐care practices related to coronavirus disease. This study aimed to review the electronic health technologies used in each of the constituent elements of the self‐care (self‐care maintenance, self‐care monitoring, and self‐care management) during the COVID‐19 pandemic. Methods This scoping review was conducted based on Arksey and O'Malley's framework. In this study, the specific keywords related to “electronic health,” “self‐care,” and “COVID‐19” were searched on PubMed, Web of Science, Scopus, and Google. Results Of the 47 articles reviewed, most articles (27 articles) were about self‐care monitoring and aimed to monitor the vital signs of patients. The results showed that the use of electronic health tools mainly focuses on training in the control and prevention of coronavirus disease during this pandemic, in the field of self‐care maintenance, and medication management, communication, and consultation with healthcare providers, in the field of self‐care management. Moreover, the most commonly used electronic health technologies were mobile web applications, smart vital signs monitoring devices, and social networks, respectively. Conclusion The study findings suggested that the use of electronic health technologies, such as mobile web applications and social networks, can effectively improve self‐care practices for coronavirus disease. In addition, such technologies can be applied by health policymakers and disease control and prevention centers to better manage the COVID‐19 pandemic.

Background and Aims The increasing elderly population, growing prevalence of chronic illnesses, and rising healthcare costs are driving healthcare providers to seek more affordable and cost‐effective care solutions. One promising approach is the integration of smart technologies within a hospital setting. This study aims to examine the transaction process toward smart hospitals and to explore the defining features, enabling technologies, and key challenges associated with digital and smart hospitals. Methods This scoping review followed the Levac, Colquhoun, and O'Brien approach. Databases including IEEE, PubMed, Web of Science, ScienceDirect, Wiley, and Scopus were searched without any time restrictions to extract relevant articles, with the final search completed on September 11, 2025. Only articles published in English were included. A qualitative synthesis method and trend analysis were applied to investigate the emergence of digital and smart hospital concepts, along with the associated technologies, features, and challenges. Results A total of 64 articles were included in this study. Assessing an organization's digital maturity is crucial for transitioning to smarter operations, encompassing structure, policies, governance, culture, and technology infrastructure. Key features of digital hospitals include the process of digitalization, system interoperability, and electronic medical records (EMRs). Transitioning to smart hospitals requires advanced technologies such as mobile networks, wireless systems, data analytics, location tracking, sensors, IoT, blockchain, and AI. The main benefits identified were improved technology adoption, stronger data management and security, enhanced organizational effectiveness, and greater digital health literacy. Additionally, six major challenges were highlighted, particularly around the adoption and integration of new technologies, data management and security, organizational barriers, and gaps in digital health literacy. Conclusion Transitioning to digital processes and smart hospital operations can streamline workflows and improve clinical outcomes. However, due to the complexity and interdisciplinary nature of healthcare technology, setting digitalization strategies and making relevant policies are crucial. Successful adoption of a smart hospital system requires collaboration among healthcare providers, technology vendors, and other stakeholders.

INTRODUCTION: The virtual reality-based (VR) game can be considered as a new approach to education and to enhance the skills of health-care students. AIMS: The purposes of this research were to design a VR game and to apply it to teach physiotherapy in neurological diseases. METHODOLOGY: In this study, at first, a VR game was designed for upper limb rehabilitation in brain-injured patients based on the literature and the opinions of physiotherapy experts and game designers. Then, the designed game was used for teaching physiotherapy in neurological diseases. Thereafter, the opinions of 31 undergraduate students about the teaching session were evaluated by two anonymous questionnaires. Data analysis was performed using descriptive statistics through SPSS (version 19). RESULTS: The VR game developed under expert supervision. The evaluation showed that the median score for students' perception of learning was 3.11. The median scores of questions related to the “facilitating level of virtual reality” and “student satisfaction” were 8.66 and 9, respectively. The analysis of students' responses to open-ended questions highlighted the therapeutic aspect of the game compared to its educational aspect. CONCLUSIONS: Application of VR games in education can enhance the students' perception of learning. Furthermore, it can provide a better understanding of physiotherapy in patients with neurological diseases as well as the satisfaction of students. However, the survey indicated that the good results of this teaching method are due to the use of VR for guiding the patient's movements.

Background: Data management related to eye injuries is vital in improving care process, improving treatment and implementing preventive programs. Implementation of a registry to manage data is an integral part of this process. This systematic review aimed to identify processes related to eye injury registries. Methods: Databases such as PubMed, Web of Science, Embase and Scopus were used in searching for articles from 2010 to Oct 2020 using the keywords “eye injuries” and” registry”. The identified processes related to eye injuries registry such as case finding, data collection, abstracting, reporting, follow-up and data quality control are presented in this review. Results: Of 1493 articles retrieved, 30 articles were selected for this study based on the inclusion and exclusion criteria. Majority of these studies were conducted in the United States. All registries had case finding and the most common resources for case finding included medical documents, reports and screening results. Moreover, majority of registries collected data electronically. However, few registries used data quality attributes to improve the data collected. Conclusion: Eye injury registry plays an important role in the management of eye injury data and as a result, better management of these data will be established. Taking into consideration that the quality of collected data has a vital role in adopting prevention strategies, it is essential to use high-quality data and quality control methods in planning and designing eye injury registries.

Introduction. In recent years, the use of dashboards in healthcare has been considered an effective approach for the visual presentation of information to support clinical and administrative decisions. Effective and efficient use of dashboards in clinical and managerial processes requires a framework for the design and development of tools based on usability principles. Objectives. The present study is aimed at investigating the existing questionnaires used for the usability evaluation framework of dashboards and at presenting more specific usability criteria for evaluating dashboards. Methods. This systematic review was conducted using PubMed, Web of Science, and Scopus, without any time restrictions. The final search of articles was performed on September 2, 2022. Data collection was performed using a data extraction form, and the content of selected studies was analyzed based on the dashboard usability criteria. Results. After reviewing the full text of relevant articles, a total of 29 studies were selected according to the inclusion criteria. Regarding the questionnaires used in the selected studies, researcher-made questionnaires were used in five studies, while 25 studies applied previously used questionnaires. The most widely used questionnaires were the System Usability Scale (SUS), Technology Acceptance Model (TAM), Situation Awareness Rating Technique (SART), Questionnaire for User Interaction Satisfaction (QUIS), Unified Theory of Acceptance and Use of Technology (UTAUT), and Health Information Technology Usability Evaluation Scale (Health-ITUES), respectively. Finally, dashboard evaluation criteria, including usefulness, operability, learnability, ease of use, suitability for tasks, improvement of situational awareness, satisfaction, user interface, content, and system capabilities, were suggested. Conclusion. General questionnaires that were not specifically designed for dashboard evaluation were mainly used in reviewed studies. The current study suggested specific criteria for measuring the usability of dashboards. When selecting the usability evaluation criteria for dashboards, it is important to pay attention to the evaluation objectives, dashboard features and capabilities, and context of use.