Data Science in healthcare
The Big Data ecosystem consists of five components: (1) data creation, (2) data collection and management, (3) analysis and information extraction, (4) hypothesis and experiment and (5) decision making and action. We propose to use data science in which systematic use of data through applied analytical disciplines (statistical, contextual, quantitative, predictive and cognitive models) leads to data-based decisions. For each segment of this ecosystem, there is a need for a customized professional education and job classification: the data engineer, data scientist and responsible for data strategy.
The role of data science in health care is triple (triple AIM): an increase in patient experience, quality and experience, better public health and a cost reduction.
The EU Data Protection Regulation (GDPR) directive recalls two objectives: better protection of personal data for individuals, and more opportunities for business in the digital single market through simplification of regulation. The implementation of this for the individual Belgian patient - with access to his health data through a consolidated platform - must be realized by May 25, 2018.
Availability, accuracy, reliability and safety are essential conditions
for added value of data science in healthcare. Data must be available
anonymously for research purposes, in such a way that the identity of
the patient is protected. The latter will be increasingly under pressure
due to technological developments.
Currently there is no legislation available to make available adequately protected patient data to parties different from traditional healthcare providers who may benefit (for research, product development ...) without the patient having to give his / her consent for a similar Purpose of use. This should take into account European regulations that assign an important role to the data controller.