PubMed · 3423523
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
J Sternon, J Vanatoru. 1987. [Self medication].. https://pubmed.ncbi.nlm.nih.gov/3423523/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.
Explore the source record for details and available documents.
A simple method is presented to estimate the prevalence for chronic diseases treated with drugs that are specific to the disease and must be taken continuously. Data required for the computations involve the total amount of drugs sold in a given region and the mean intake of these drugs, which can be based on the defined daily doses, the prescribed daily doses, or the consumed daily doses. The prevalence of the studied disease is the most accurate using the consumed daily doses estimated in a sample of patients with the disease of interest. Data on diabetes mellitus and intraocular hypertension collected in Belgium in 1990 and 1992, respectively, were used to illustrate the applicability of this quick and inexpensive method. For these two diseases, the precision of the prevalence estimated with the present method is comparable to that achieved with conventional cross-sectional studies, but at a lower cost.