  Currently, there are many difficulties regarding the interoperability of
medical data and related population data sources. These complications get in
the way of the generation of high-quality data sets at city, region and
national levels. Moreover, the collection of datasets within large medical
centers is feasible due to own IT departments whereas the collection of raw
medical data from multiple organizations is a more complicated process. In
these circumstances, the most appropriate option is to develop digital products
based on microservice architecture. Because of this approach, it is possible to
ensure the multimodality of the system, the flexibility of the interface and
the internal system approach, when interconnected elements behave as a whole,
demonstrating behavior different from the behavior when working independently.
These conditions allow, in turn, to ensure the maximum number and
representativeness of the resulting data sets. This paper demonstrates a
concept of the platform for a sustainable generation of quality and reliable
sets of multimodal medical data. It collects data from different external
sources, harmonizes it using a special service, anonymizes harmonized data, and
labels processed data. The proposed system aims to be a promising solution to
the improvement of medical data quality for machine learning.
