Data Silos and Digitalization Gaps

• Much of German healthcare was historically paper-based or has fragmented IT systems
• Accelerating healthcare digitalization is urgent – an area where Germany lags
• If EHRs and data interoperability aren't in place, AI algorithms cannot access needed data
• Data silos within hospitals (lab, radiology, notes in separate systems) hinder AI development

Resource and Expertise Limitations

• Survey of German hospitals identified lack of resources as the primary barrier:
  • Shortage of skilled AI staff and data scientists
  • Limited technical know-how for implementation
  • Insufficient financial support for AI projects
• Few hospitals have in-house AI experts to develop or manage AI initiatives
• Many facilities have no clear AI strategy or roadmap
• Smaller hospitals especially may see AI as a luxury amid other pressing needs

Integration and Workflow Challenges

• Seamlessly fitting AI into clinical workflow is non-trivial
• If using AI is cumbersome or disruptive, clinicians will resist adoption
• User interface and interoperability are critical success factors
• AI must slot into existing software (EHRs, imaging systems) to be useful
• Staff training requirements add complexity to implementation

Trust, Acceptance and Cultural Factors

• Medicine is conservative regarding patient safety—clinicians are cautious about new tools
• Building trust in AI requires evidence, transparency, and time for culture change
• Some staff fear AI might replace them, leading to resistance
• Patient acceptance varies, with some discomfort about algorithms in their care
• Hospitals must engage in education for both staff and patients about AI benefits