PubMed · 11637573
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K Rákóczi. 1984. [Not Available].. https://pubmed.ncbi.nlm.nih.gov/11637573/
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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.
Psychosocial characteristics have been linked to coronary heart disease. In the Belgian Job Stress Project (1994-1999), the authors examined the independent role of perceived job stress on the short-term incidence of clinical manifest coronary events in a large occupational cohort. A total of 14,337 middle-aged men completed the Job Content Questionnaire to determine the dimensions of the extended job strain model, job demands, decision latitude, and social support. Jobs were categorized into high strain, low strain, active jobs, and passive jobs. During the 3-year follow-up, 87 coronary events were registered. At baseline, 17% of workers experienced high strain. Job demands and decision latitude were not significantly related to the development of coronary heart disease after adjustment for covariates. The 38% risk excess among subjects classified in the high-strain category did not reach statistical significance. However, coronary heart disease incidence was substantially associated with the social support scale independently of other risk factors, with an adjusted hazard ratio of 2.4 (95% confidence interval: 1.4, 4.0) between extreme tertiles. No convincing evidence for an association of job demands, decision latitude, or job strain with the short-term incidence of coronary heart disease was found. However, our study underscores the importance of a supportive social work environment in the prevention of coronary heart disease.
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