Focus area of Data Science

Health Analytics

Improving your health through data analytics

Health analytics enables personalized care delivery throughout the care continuum

Health analytics is transforming health services owing to the advent of digitization and new information collection systems (e.g. patient portals, IoT, cloud computing, big data, and wearables), so that health innovation is driven by analyzing, processing and acting on health data. Data science is the key element of this revolution, allowing to merge, analyze and process all health related data to gain more actionable insight, understanding and knowledge at individual and population level. This provides the basis for modern innovation and value addition in evidence-based medicine.

In the future, the availability of large volumes of health data is an important asset for health organizations. Data, controlled by the citizens, are collected ubiquitously throughout the care continuum, connected seamlessly and interpreted within the right context. Data are used to improve health solutions and to advance medical knowledge, leading to better outcomes while increasing efficiency. For instance, data are continuously used to analyze and improve workflows and medical guidelines, thereby providing stronger evidence for best practice solutions. For this purpose, health analytics enables personalized care delivery throughout the care continuum. Using personalized health records citizens are empowered to control their own health better, such that the collected data is of service to the individuals and to the society at large.

Meet our Core Team

Research Challenges

We research how harmonizing and integrating sensitive health data on a large scale can be done, as this is key to efficient health analytics. This includes research on distributed models, as data can often not be collected at one central location. We investigate data-driven and personalized decision-support models to support both individuals and health professional. We also analyze the effectiveness of clinical protocols and clinical pathways. To support value-based healthcare, we develop models that continuously learn and adapt in order to facilitate using them in practice.

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