Missing interoperability and a fragmented IT-landscape often are considered as the reason of slow digital health adoption. But those buzzwords are only the consequences of a bigger mindset in the health-IT environment.
There is nothing and no one to blame for. It’s just an analysis out of decades of experience in a very distinguished market.
Health-IT involves critical infrastructure, applications and solutions. When some core health solutions fail to work there instantly would be loss of lives or suffering as results.
Therefore technical staff in hospitals, responsible for the trouble-free operation of the IT infrastructure and sitting at the heart of health data production, are very engaged in ensuring the full functionality of all systems.
In a market of billions of EUROs spent every year, this makes them the holy grail holders for all the different solution providers.
A hospital IT-architecture is very complex. There are hundreds of different applications which have to smoothly work together and changing one thing at one end of the moloch could result in different errors at several sites. The architectures have been growing over the years, changing responsible persons on site included. Lots of different IT-projects are running simultaneously and only few people if any are watching and overseeing the whole picture.
Putting ourselves into the shoes of an IT-responsible within a hospital and facing a market of dynamically changing digital health solutions including the stated facts above – what would you do?
The safe way to go would be change as few things as possible and maintaining the (in their eyes secure) status quo. And this is exactly what most hospitals do.
This is one of the main reasons why slow digital health adoption, missing interoperability and fragmented IT-architectures result as a consequence.
But there are other reasons as well. As long as digital health services can’t be charged for and health digitization is considered as a local, organization specific phenomenon, we won’t be able to change anything soon.
More than 30 years ago, Frederic Vester, one of the frontrunners of the modern cybernetic theory, invented the game “ecopolicy”. It would be very interesting to see how “healthopolicy” would look like. An ecosystem of health stakeholders and different adjusting screws of governmental, economic and digital indicators in a more or less regulated world.
Fact is that health digitization is not an isolated topic concerning a single organization. Digitization in health is exchange and collaboration of information between different stakeholder within an ecosystem. This predefines a fluent and continuously working solution which is able to send and accept data from peers outside of the own organization in a structured and high quality way.
That’s the reason why PaaS platforms are growing. There have to be solutions in place filling the gaps between the local IT-architectures of different organizations. But filling the gaps wouldn’t be enough. That’s no guarantee different systems of various organizations would be able to talk to each other in an understandable way. Therefore PaaS has to offer a “data broker” or “translator” where diverse structured data from different organizations and systems can be standardized and exchanged.
FHIR as the new data exchange standard is a good start. But most of the health applications don’t “speak” FHIR, are based on individual HL7 v.XVW and also need a translator in between (OpenEHR and an integration engine like Mirth).
healthbrain (www.healthbrain.online) very much believes in “high quality health data” as the source of improved collaboration, exchange and interoperability. A health system which founds on the premises of “garbage-in -> garbage-out” will always need additional investments and solutions to partly make health data exchangeable which is very cost intense. healthbrain focuses at the creation of health data where health data already has to be produced in the best quality possible and be ready for a later exchange with peers and partners within an ecosystem. This is the basis for improved medical processes, pathways and workflows as well as data analysis and data aggregation and eventually for better health outcomes.
