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Artifical Intelligence
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.
28 voices from nursing practice
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.
What is quality of care really? Three dimensions in the field of tension
"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.
The three perspectives of care quality
Three different perspectives emerged from the discussions:
- External: MD criteria and regulatory requirements
- Internal: What each facility defines for itself as the house standard
- Residents: How quality is subjectively perceived - "what the person receiving care perceives for themselves"
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.
Two streams of development in care
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.
Expert standards and AI: The bridge is there - but it is not always used
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.
Where AI is successfully applied in the expert standards
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"
AI as a support, not a replacement - clear boundaries from practice
The message from the participants was clear: AI should support, but never replace.
Positive AI examples from practice
- 3D wound analysis for precise description and assessment
- SIS and proposed measures (as with LINDERA) "give people security and they are not alone"
- Support for young professionals after training and foreign staff
- Quality standardization: "With AI, they should be brought into line" despite different histories and professional experience
Clear requirements for AI systems
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."
Red lines: What must remain human
"What must not be replaced is that despite AI, every professional still needs their own care knowledge"
- Case discussions and interpersonal processes remain human
- AI can support quality, but not take over
- The final decision always lies with the human being
The decisive success factor: the benefits for employees
Key finding: "The driver for AI is when the employee recognizes the benefits."
What care staff really need
- "What supports me in the care process?" is the key question
- The cost-benefit factor must be right
- Without incentivization for quality, listlessness sets in
Two levels of evaluation
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 makes us at LINDERA think
"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.
Our three most important learnings
- AI must create time for what is important - not just become more efficient
- Objectivity and individuality must be in balance
- The benefit for the carer determines acceptance and success
What happens next: continuation of the webinar series
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.
Quality of care needs dialog
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.
