Objectives: The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.
Materials And Methods: The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence.
The German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS.
View Article and Find Full Text PDFBackground: Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent.
View Article and Find Full Text PDFBackground: The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research.
View Article and Find Full Text PDFBackground: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI.
View Article and Find Full Text PDFBackground: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.
View Article and Find Full Text PDFBackground: Provenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research.
View Article and Find Full Text PDFBackground: In almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse.
View Article and Find Full Text PDFBackground: Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities.
View Article and Find Full Text PDFBackground: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys.
View Article and Find Full Text PDFBackground: One of the key strategies for reducing maternal and perinatal morbidities and mortalities is the provision of skilled intrapartum care. While cesarean section is an important emergency obstetric intervention for saving the lives of mothers and newborns, a study comparing the prevalence of cesarean delivery is not sufficiently available in Ethiopia. This study aimed at assessing the prevalence and associated factors of cesarean delivery among women who gave birth at hospitals in Dessie town, Northeast Ethiopia.
View Article and Find Full Text PDFBackground: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes.
View Article and Find Full Text PDFBackground: Population-level survey is an essential standard method used in public health research to quantify sociodemographic events and support public health policy development and intervention designs with evidence. Although all steps in the survey can contribute to the data quality parameters, data collection mechanisms seem the most determinant, as they can avoid mistakes before they happen. The use of electronic devices such as smartphones and tablet computers improve the quality and cost-effectiveness of public health surveys.
View Article and Find Full Text PDFArch Public Health
September 2018
Background: Absence of reliable health insurance schemes is a key challenge to meet the universal health coverage target of the Sustainable Development Goals (SDGs). Ethiopian health system is characterized by under financing, low protection mechanisms for the poor, and lack of mechanisms of risk pooling and cost sharing. Ethiopia is implementing social health insurance (SHI) scheme to reduce out of pocket payment (OOP) and improve access and use of healthcare.
View Article and Find Full Text PDFIntroduction: Accessing family planning can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive methods is low but it is increasing. This study aimed to analyze the trends and determinants of changes in modern contraceptive use over time among young married women in Ethiopia.
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