Expert standards, AI and the bridge between theory and practice - our key findings from today's LINDERA webinar with quality managers, nursing specialists and managers.
Our webinar "Expert standards and AI - where is the bridge?" was fully booked within a few days. 28 participants from quality management, nursing service management and direct nursing practice discussed the future of care quality with us.
Why is this topic so important? As the LINDERA team, we face the challenge every day: how do we bring together evidence-based care quality and modern AI technology? The answers from our participants have opened up new perspectives for us.
"For me, quality of care means that the person receiving care feels safe, respected, well cared for and individually looked after thanks to professional expertise, attention and good communication" - this contribution from one participant sums up the complexity.
Three different perspectives emerged from the discussions:
This multi-layered approach makes it clear that there is no single definition of quality of care. And this is precisely where one of the greatest challenges for AI systems in the care sector lies.
At LINDERA, we are currently observing two different approaches:
The first stream: facilities that already create quality on their own and incorporate AI as an additional tool. These teams see technical support as an opportunity to optimize their already good work.
The second stream: organizations that have not yet made the leap to systematic quality work. Here, it is often hoped that external solutions will solve the problem - but without internal structures, even the best technology will not work.
Result: Expert standards are the professional basis - "must be fulfilled" - and AI can help with this. But there is a discrepancy between knowledge and action.
Evidence-based support: AI can measurably improve the professional quality of standards
Time-saving: Automated analyses, decision templates and action plans relieve the burden on nursing staff
Objectivity : "AI should provide objective criteria"
The message from the participants was clear: AI should support, but never replace.
No direct takeover : "Direct takeover must not happen"
Decision support: "What can be incorporated into the care process? What supports the resident?"
Time: "How can I achieve the quality I want in the shortest possible time? Above all, consistent quality."
"What must not be replaced is that despite AI, every professional still needs their own care knowledge"
Key finding: "The driver for AI is when the employee recognizes the benefits."
Nursing staff evaluate according to practical benefits in everyday work
Commercial management calculates the cost-benefit factor
Both levels need to be convinced - but with different arguments.
"What is care if everything is 100% efficient? How are the employees doing, how are the residents doing?"
This honest reflection concerns us. Efficiency alone cannot be the answer.
The great interest shows: The dialog between nursing practice and AI development must continue. We are planning to continue this webinar series - also with specific topics requested by the community.
Our most important learning: quality of care in the age of AI is not created by technology alone, but through continuous dialog between developers and practitioners.
The 28 participants showed us that AI can improve the quality of care - but only if it empowers people, not replaces them.