‘Healthcare', 'hospitals' and 'patient records' are probably not terms you immediately associate with KPN. Yet the technology company plays an all-important role in the world of medical data with KPN Health Exchange. What technologies are used in this platform? And what does the future hold?
A lot has happened in healthcare in recent years. In particular, the 'compartmentalization', whereby various agencies and care providers have access to medical information, but do not always exchange it efficiently, causes unnecessary delays. KPN's Health Exchange allows healthcare organizations to exchange medical information more quickly, easily, and securely. This is a SaaS platform that is connected to various agreement systems in the healthcare sector.
A system like Health Exchange has several advantages. Information is easier to share between healthcare professionals, medical information is more transparent for patients and healthcare professionals experience less administrative burden. To make the last point concrete: instead of constantly having to fill in forms, data is already recorded digitally and in a structured manner.
As a former nurse, Teun - now Information Manager at KPN Health - tries to link digital technology to the human dimension. He has only been with KPN for six months but was seduced by its vision of using medical data. One thing is certain: interpreting patient data is becoming increasingly important.
Discovering and analyzing correlations in health data
Anyone who thinks that healthcare institutions do not have their data in order is wrong. Hospitals, GPs, care institutions, etc. often have their patient data in order. Health Exchange, however, ensures that the data can 'talk' to each other, as it were. Teun: 'We don't do anything with the data ourselves, but we do ensure that it can be better accessed so that the various healthcare professionals can exchange it with each other.’ Ultimately, professionals must not only use all the data from all the different healthcare institution source systems to see how patients are doing, but also what the data tells them. What can they learn from the data? Can certain connections be made? And can certain things be predicted?