The advantages, disadvantages, threats, and opportunities of electronic patient-reported outcome systems in cancer: A systematic review.

Objective: Electronic patient-reported outcome (ePRO) systems hold promise for revolutionizing communication between cancer patients and healthcare providers across various care settings. This systematic review explores the multifaceted landscape of ePROs in cancer care, encompassing their advantages, disadvantages, potential risks, and opportunities for improvement. Methods: In our systematic review, we conducted a rigorous search in Scopus, Web of Science, and PubMed, employing comprehensive medical subject heading terms for ePRO and cancer, with no date limitations up to 2024. Studies were critically appraised and thematically analyzed based on inclusion and exclusion criteria, including considerations of advantages, disadvantages, opportunities, and threats. Findings: Analyzing 85 articles revealed 69 themes categorized into four key areas. Advantages (n = 14) were dominated by themes like "improved quality of life and care." Disadvantages (n = 26) included "limited access and technical issues." Security concerns and lack of technical skills were prominent threats (n = 10). Opportunities (n = 19) highlighted advancements in symptom management and potential solutions for technical challenges. Conclusion: This review emphasizes the crucial role of continuous exploration, integration, and innovation in ePRO systems for optimizing patient outcomes in cancer care. Beyond traditional clinical settings, ePROs hold promise for applications in survivorship, palliative care, and remote monitoring. By addressing existing limitations and capitalizing on opportunities, ePROs can empower patients, enhance communication, and ultimately improve care delivery across the entire cancer care spectrum.

Effectiveness of serious games in social skills training to autistic individuals: A systematic review.

Autism Spectrum Disorder (ASD) is a complex neurological condition that significantly impacts individuals' daily lives and social interactions due to challenges in verbal and non-verbal communication. Game-based tools for psychological support and patient education are rapidly gaining traction. Among these tools, teaching social skills via serious games has emerged as a particularly promising educational strategy for addressing specific characteristics associated with autism. Unlike traditional games, serious games are designed with a dual purpose: to entertain and to fulfill a specific educational or therapeutic goal. This systematic review aims to identify and categorize serious computer games that have been used to teach social skills to autistic individuals and to assess their effectiveness. We conducted a comprehensive search across seven databases, resulting in the identification and analysis of 25 games within 26 studies. Out of the 104 criteria assessed across these studies, 57 demonstrated significant improvement in participants. Furthermore, 22 of these studies reported significant enhancements in at least one measured criterion, with 13 studies observing significant improvements in all assessed outcomes. These findings overwhelmingly support the positive impact of computer-based serious game interventions in teaching social skills to autistic individuals.

Developing a prediction model for successful aging among the elderly using machine learning algorithms.

Objective: The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods: The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results: Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions: Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

Security Requirements of Internet of Things-Based Healthcare System: a Survey Study.

INTRODUCTION: Internet of Things (IoT), which provides smart services and remote monitoring across healthcare systems according to a set of interconnected networks and devices, is a revolutionary technology in this domain. Due to its nature to sensitive and confidential information of patients, ensuring security is a critical issue in the development of IoT-based healthcare system. AIM: Our purpose was to identify the features and concepts associated with security requirements of IoT in healthcare system. METHODS: A survey study on security requirements of IoT in healthcare system was conducted. Four digital databases (Web of Science, Scopus, PubMed and IEEE) were searched from 2005 to September 2019. Moreover, we followed international standards and accredited guidelines containing security requirements in cyber space. RESULTS: We identified two main groups of security requirements including cyber security and cyber resiliency. Cyber security requirements are divided into two parts: CIA Triad (three features) and non-CIA (seven features). Six major features for cyber resiliency requirements including reliability, safety, maintainability, survivability, performability and information security (cover CIA triad such as availability, confidentiality and integrity) were identified. CONCLUSION: Both conventional (cyber security) and novel (cyber resiliency) requirements should be taken into consideration in order to achieve the trustworthiness level in IoT-based healthcare system.

The impact of health information exchange on healthcare quality and cost-effectiveness: A systematic literature review.

BACKGROUND AND OBJECTIVE: Health Information Exchange (HIE) is known as a technology that electronically shares all clinical and administrative data throughout healthcare settings. Despite this technology has a great potential in the healthcare industry, there is a limited and sparse evidence of articles which illustrated the impact of HIE on quality of care and cost-effectiveness. This work presents a systematic review that evaluates the impact of HIE on quality and cost-effectiveness, and the rates of HIE adoption and participation in healthcare organizations. METHODS: We systematically searched all English papers that were indexed in four major databases (Science Direct, PubMed, IEEE and Web of Science) between 2005 and 2016. Consequently, 32 identified papers appeared in 21 international journals and conferences. Eligible studies independently were critically appraised, collected within data extraction form and then thematically analyzed by two reviewers and if necessary, the third author. The selected papers have been classified based on 11 main categories including publication year, journal and conference names, country and study design, types of data exchanged, healthcare levels, disease or disorder, participants in organizations and individuals, settings characteristics and HIE types, the impact of HIE on quality and cost-effectiveness, and the rates of HIE adoption and participation. RESULTS: Of the 32 articles, 25 studies investigated the financial and clinical impact of HIE. Overwhelmingly, HIE studies have reported positive findings for quality and cost-effectiveness of care. 15 of HIE studies (60%) demonstrated positive financial effects and 16 studies (64%) reported positive effects on quality improvement of patient care. However, the overall quality of the evidences was low. In this regard, cohort study (59.38%) was the most common used study design. Nine studies presented the rates of HIE adoption and participation. The lowest and highest participation rates were 15.7% and 79%, respectively. CONCLUSIONS: HIE can be considered as a superior potential for healthcare information system, resulting to promote patient care quality and reduce costs related to resource utilization. However, further researches are needed in order to provide a better understanding of this domain and accordingly attain new opportunities to increase users' participation and motivation for successfully adopting this technology.