The prospects presented by digital transformation holds the potential of greatly increasing the capacity of medical libraries, which in turn it will make it possible for these libraries to provide... Show More

The prospects presented by digital transformation holds the potential of greatly increasing the capacity of medical libraries, which in turn it will make it possible for these libraries to provide better services and increasing accessibility. But how can digital technologies be adopted in transforming medical libraries' services and operations, in the digital transformation era? This study looked at the opportunities and challenges associated with implementing digital technologies in medical libraries through digital transformation. The study thus reviewed literature on digital transformation of medical libraries services, as a way to support healthcare systems in South Africa. The chapter revealed that digital transformation has the potential to enhance libraries processes, practices, performance, and to stay relevant in their increasingly competitive environment.

This systematic review aims to provide insight into ChatGPT's application in health communication. The review, based on seven academic databases, employed Boolean operators to connect keywords... Show More

This systematic review aims to provide insight into ChatGPT's application in health communication. The review, based on seven academic databases, employed Boolean operators to connect keywords, refining search results. Selection criteria and quality assessment ensured a comprehensive evaluation. Fourteen articles were included, focusing on the application of ChatGPT in interpersonal health communication. Survey research methods were mostly used, revealing four main application advantages and ten application risks, such as providing inaccurate and incomplete health information. At present, ChatGPT is still not suitable for widespread application in health communication. Diverse linguistic and cultural contexts, interdisciplinary collaboration, real patient experiences, integration of ChatGPT with existing healthcare information systems, and some ethical factors must be considered to ensure ChatGPT effectively empowers health communication.

This descriptive manuscript describes challenges, solutions, and recommendations to provide a supportive framework for clinician researchers striving to develop a clinical app. The Back2Play app... Show More

This descriptive manuscript describes challenges, solutions, and recommendations to provide a supportive framework for clinician researchers striving to develop a clinical app. The Back2Play app aims to support youth in concussion recovery through mobile health monitoring. Developed in Phase 1 of a randomized control trial, the app intends to prevent re-injury and improve recovery by integrating evidence-based return-to-school and activity guidelines with biological monitoring. Methods outline the stages of successful app development. Identified challenges include defining app requirements, database hosting, development, and legal procedures. Results of usability testing show that the Back2Play app was highly acceptable to 66% of youth and none found it unacceptable. Successful clinical app development hinges on understanding the scope of the app, data management and exportation. Adequate funds should also be allocated for potential delays. These recommendations provide a framework for clinical researchers hoping to develop a clinical app that achieves their envisioned outcome.

Chronic kidney disease (CKD) is a medical condition characterized by impaired kidney function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early... Show More

Chronic kidney disease (CKD) is a medical condition characterized by impaired kidney function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early diagnosis and treatment have been made. However, as diagnosis is time-consuming, an automated system is necessary. Researchers have been employing various machine learning approaches to analyze extensive and complex medical data, aiding clinicians in predicting CKD and enabling early intervention. Identifying the most crucial attributes for CKD diagnosis is this paper's primary objective. To address this gap, six nature-inspired algorithms and nine machine learning classifiers were compared to evaluate their combined effectiveness in detecting CKD. A benchmark CKD dataset from the UCI repository was utilized for this analysis. The proposed model outperforms other classifiers with a remarkable 99.5% accuracy rate; it also achieves a 58% reduction in feature dimensionality. By providing a reliable, cost-effective tool for early CKD detection, the authors aim to revolutionize patient care.