Guest post by Jarmo Pääkkönen from
Centre for Health and Technology, University of Oulu
The MIDAS platform has been developed for policy making and data analytics from various health-related data sources. The solution has been targeted basically for health and welfare area decision making. The reuse of the platform and its components are also one important aspect to be considered. There are different areas of decision making that the platform could be reused easily, and not only in the area of health.
The simplest way is to use the tool in close collaboration with its original purpose by enriching the data sets with some new data in the area. The earlier findings in any of the four MIDAS pilots for one policy aims could show some new results with some new data. This way, there could be more evidence to support earlier policy making. The use of data in the social services area could be useful as social care is very much related to health policy. It must also be remembered that new data can even show somewhat opposite or conflicting findings compared to earlier results. However, that might be the case even without any platform is used in decision making. People tend to be easily misled by unmeaningful or unwanted information – sentimental knowledge is not supported by platforms.
The MIDAS platform could be used and developed for strategic planning of the cities, and policy areas could be diverted from the health and social side into any city-level decision making. The area of building and land use planning would be very fruitful grounds. The platform could be used for housing policy, e.g. for a new hospital, sports arena and shopping mall planning related decision. The data then do not need to be personal data, so access is easier for real estates and urban area planning than for health. The data access in natural resources and energy policy would be even more simple, as that does not need any personal data. There is useful statistical data and open sensory information available as open data about the environment status and history e.g. from weather station information.
The decision making could be enhanced with automated data analytics and machine learning algorithms. This is not available in MIDAS but could be developed in a similar way as these have been taken into use in different areas. One of the interesting areas used is the milk production of cows by optimising the meal times and amounts together with optimal weather conditions for cattle grazing in the fields. Could this kind of big data approach perhaps be used for optimising, e.g. car traffic and public transportation services – what about even for health policy cases?
There are a few MIDAS platform tools like the MIDAS Medline tool , News Dashboard , MIDAS Twitter chatbot tool  and GYDRA data preparation tool . The tools by themselves are also useful as such and could be useful for a new project as well. The MIDAS environment and its tools could be piloted and demoed in hackathons for other interesting new purposes and areas. Of course, it would increase the use of the platform and its tools. The MIDAS hackathons could offer an interesting big data playground for showing its new potential. The learnings from hackathon pilots could be taken forward for increasing the understanding of the whole concept, and it could even help with the new project establishment.