EPIDEMIOLOGY, BIG DATA & REAL-WORLD EVIDENCE – RWE
Epidemiological analysis of health phenomena is a powerful base instrument for decision support at all levels of our NHS. The normal functioning of this is linked to a continuous production of data that integrate huge databases of information (big data) reflecting an activity and everyday reality in the field (RWE). The combination of this information with a proof of classic scientific studies allows to create a solid and detailed knowledge system, crucial for the correct and efficient functioning of the NHS.
ARTIFICIAL INTELLIGENCE IN HEALTH
Artificial Intelligence (AI) – here understood as machine learning (ML) – is a technique that differs from traditional programs in that it learns through examples – and not through computer rules – changing its structure during that learning (deep learning). This process is particularly well adapted to the predictive needs of the NHS, managing to establish very complex relationships between data and results in a way that no human being can. One of the most important fields is that of image classification, but there are numerous other fields of application of AI in health: continuous monitoring of signs, clinical notes, genetic data, diagnostic processes, etc.
HEALTH OUTCOMES RESEARCH – HOR AND HEALTH SERVICES RESEARCH – HSR
The HOR / HSR scientific methodology studies the final result of healthcare, that is, the effects of the care implementation process on health indexes, as well as on the well-being of patients and populations. It includes a varied set of areas, from studies analyzing the effectiveness of a medicine to the effect of the reimbursements on health results, from the information thus obtained as a basis for the decision, to the impact of health technologies (to name just a few topics).
PUBLIC HEALTH & PUBLIC POLICIES
Public Health (PH) can be defined as the multidisciplinary area of health promotion through local or global community initiatives. Its scientific bases are based on three areas of knowledge: basic sciences (pathology, toxicology, etc.), clinical sciences (internal medicine, pediatrics, pharmaceutical sciences, etc.) and specific sciences such as epidemiology, environmental health, health education , etc. One of the most important applications of PH is health policies, supporting decisions, analysing results, predicting future changes, etc.
HEALTH TECHNOLOGY ASSESSMENT
The introduction of new technologies in the NHS – be they drugs, equipment, surgical interventions, design of physical spaces, standards of clinical practice, etc. – must comply with detailed analyzes of its benefit, risk and costs. The comparison with the technologies already available in a given context implies an analysis of the characteristics of the new technology (drug, for example), its extrapolated effects from clinical trials, its indications in specific sub-groups of patients and its increased costs. The derivation of specific indices through mathematical and statistical models (in the case of medicines, the Quality Adjusted Life Years – QALYS and the Incremental Cost Effective Ratio – ICER) allows defining the cost effectiveness of the technologies.
The dimension of the biomedical scientific literature, together with its quality variable, makes it difficult to use the most relevant evidence generated by clinical studies in practice. Furthermore, the understanding of the results obtained is hampered by the almost total mastery of statistical and formal concepts that a good part of health professionals, managers and administrators, politicians and especially patients and their families present. The field of Knowledge Translation has as main objective to present evidence data in an understandable way to all health stakeholders, in order to better support their decisions.