Peter Haas, an expert in telematic health solutions, has designed solutions that support medical action based on knowledge. He has carried out numerous research and development projects and teaches various subjects in medical informatics. However, in a recent interview, Haas explains why he believes the digitization of the healthcare system fails.
The biggest hurdles in introducing the electronic patient record (ePA) is the lack of focus on benefits and usability. In the case of the ePA, political actors and healthcare stakeholders have their own agendas that result in numerous hurdles, which lead to the political attempt to increase the number of interested citizens via the back door with this opt-out regulation.
The poor acceptance of ePA’s so far is due to the fact that doctors have a lot more work to do, and there is no clear benefit for the patients. A conceptual solution for ePA involves using a more process-oriented treatment management platform. The platform should support the medical profession, nursing staff, and everyone affected in the way they can and need it.
The federals government’s plan to start the ePA, whereby medical findings can be stored as a PDF, is not an intelligent solution since digitizing something without a usage-oriented structure does not make sense. Haas speaks of a patient-phenomeno-ontological file in this case that goes beyond documents.
The ePA has no practical value until the most important things from the history of the disease are recognizable on another level and can be updated. The solution involves thinking more about the inner structure of the file for good use and usability.
Citizens’ acceptance of ePA is low because it is too complicated and cumbersome to apply for even university students, and people are afraid that their illness histories may be stigmatized. Structured data could establish a causal connection between taking a drug and the state of health, but the data used must be valid and comparable.
In conclusion, when researching with routine data, one must be careful not to compare apples with oranges and neglect contextual conditions. To draw ethically justifiable conclusions, data must be comparable and reliable.
