PubMed · 4578764
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R Weekers. 1972. [Max Humblet].. https://pubmed.ncbi.nlm.nih.gov/4578764/
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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.
For several decades, periodical dredging of river sediments has been necessary to allow for shipping traffic on the river Scheldt. Sediments were disposed along the shores in the alluvial plain without concern for the potential presence of contaminants. The aim of this study was to survey the alluvial plains of the Upper Scheldt river in Belgium for the presence of old dredged sediment landfills, and to appraise the heavy metal contamination at these sites. Up to 82% of the areas that were affected by dredged sediment disposal was found to be polluted by at least one of the metals Cd, Cr, Zn or Pb. Concentrations of Cd, Cr and Zn were, in 10% of the cases, higher than 26, 1900 and 2800 mg/kg, respectively. Cu and Ni concentrations were of no environmental concern on any site. Trends in metal concentrations as a function of location and time were explored and discussed. The highest average concentrations of Cd, Cr, Cu, Pb and Zn were found in the most downstream quarter of the Upper Scheldt. Contents of Pb and Cu were significantly lower for sediments disposed after 1965, but no indication for improvement of the sediment quality with time was observed for Cd, Cr and Zn. The pollution levels encountered warrant for caution as most of the soils affected by historical dredged sediment disposal are currently in use for agriculture, nature development or forestry.
Health-care providers, insurance agencies, ministries, clinicians etc. are installing Information and Communication Technology to monitor patients, patient care and pharmaceutical drug-prescribing patterns. The ever-growing databases create opportunities for better knowledge about the health-care system and, eventually, more effective management. Yet, these databases run the risk of under-utilisation or simplistic interpretation through lack of appropriate knowledge methodology. On the one hand, managers and policy makers oversimplify the available information. Numerous individual patients and pharmaceutical drugs are classified into a small number of categories and measured using Great British pounds or Euros. Sums, averages and two-way tables cover up all the fine shades in patient problems and therapies that are recorded in great detail. On the other hand, investigations discover a wide diversity of patients and a great variability of care patterns among clinicians, hospitals and regions. When so-called unexplained regional, institutional and clinician differences are published, confusion and distrust arise. Examples exist in France, the USA, the UK and Belgium. This document investigates how to discover management knowledge with respect to drug prescribing patterns. The example presented covers the cases of the 18,000 Belgian home practitioners.