Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Skills and competencies in health data analytics for health professionals: a scoping review protocol
7
Zitationen
4
Autoren
2023
Jahr
Abstract
INTRODUCTION: Healthcare data analytics is a methodological approach to the systematic analysis of health data, and it provides opportunities for healthcare professionals to improve health system management, patient engagement, budgeting, planning and performing evidence-based decision-making. Literature suggests that certain skills and/or competencies for health professionals working with big data in health care would be required. A review of the skills and competencies in health data analytics required by health professionals is needed to support the development or re-engineering of curriculum for health professionals to ensure they develop the abilities to make evidence-based decisions that ultimately can lead to the effective and efficient functioning of a healthcare system. METHODS: Using Arksey and O'Malley's framework, this study will review literature published in English from January 2012 to December 2022. The database search includes Academic Search Complete, CINAHL, and MEDLINE via EBSCOhost, PubMed, Science Direct, Scopus, and Taylor and Francis. The reference lists of key studies will be searched to identify additional appropriate studies to include. The review will be conducted using an inclusion and exclusion criteria. Iterative processes will be involved at the various stages of search strategy piloting, screening and data extraction. Articles will be reviewed through a two-step process (title and abstract, and full-text review) by at least two reviewers. Data will be described quantitatively and/or qualitatively and presented in diagrams and tables. ETHICS AND DISSEMINATION: Ethical clearance has been received, and strict protocol measures will be followed to ensure the data reported is of quality and relevant to the review purpose. The results will be disseminated through a peer-reviewed scientific journal, presentation at national and/or international conferences, and other platforms such as social media (eg, LinkedIn, Twitter), and relevant stakeholders.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.