About

In modern health care, various data types are becoming increasingly available for medical doctors, researchers, and also patients themselves. New technologies, such as genomic sequencing, provide data to understand diseases on individual level. At the same time, new legislation enables more efficient use of hospital registers and other health care data to serve medical research and patient care. This increase in data availability requires actions between experts in health and data science to establish practices for efficient data analysis and interpretation. For example choosing treatment for a patient can be assisted by comparing patient samples to a broad set of population samples using artificial intelligence.

Health data science paves the way to more personalised treatments of diseases. It also gradually changes the focus of medicine from curing diseases to predicting and preventing them.

Health data science covers essentially all aspects of science where data is analysed in order to benefit human health. These data include, among other, biomedical and clinical research data from patients (e.g. molecular and imaging data), clinical data from hospitals and other health care or population registers (Real-World Data), and different ways of monitoring self and well-being, such as data from smart consumer devices.

Health data science encompasses, among other, combining and harmonising data from multiple sources. It also encompasses generating, testing and validating algorithms for different purposes, such as data modeling, data analysis, diagnosis, or facilitating decision making in health care.

The domain of health data science can gradually enable predicting diseases before onset through identifying risk factors and biomarkers in populations, preventing diseases before onset through identifying required changes, and finding optimal personalised treatments.