May 22, 2025

Lizzy Herzer
Lizzy Herzer
In a time when nursing shortages are becoming increasingly urgent, Artificial Intelligence (AI) offers promising possibilities to relieve nursing staff and improve quality of care. But how can AI be safely and meaningfully implemented in nursing homes? Which use cases are already feasible today? And what ethical and legal aspects must be considered?
Potential of AI in Nursing Homes
The use of AI in care can offer numerous advantages:
Time savings: Routine tasks like documentation can be partially automated
Quality assurance: Systematic monitoring of care processes
Decision support: Risk assessments and recommendations for action Translation into over 20 languages
Staff relief: More time for direct resident care especially in times of skilled labor shortages, AI can help improve working conditions and ensure quality of care.
Practical Use Cases for AI in Care
1. Digital Care Documentation
Care documentation consumes much of staff time daily. AI can support by:
Automatic creation of care reports based on templates Intelligent text modules adapted to individual resident needs
Detection of inconsistencies or missing information Integration of the structural model to reduce bureaucracy in care documentation
Practical example: A caregiver enters brief notes about the measure performed, and the AI creates a complete, professionally correct documentation entry that only needs to be reviewed and confirmed.
2. Medication Management
The safe administration of medications is a critical area:
Creation and updating of digital medication plans
Checking for interactions and contraindications
Reminder functions for medication administration
Documentation of medication intake
Practical example: An AI-supported app creates a clear medication plan based on medical prescriptions, points out possible interactions, and documents administration.
3. Quality Assurance and Testing
AI can support quality assurance in nursing homes:
Creation of checklists for internal quality checks
Automatic evaluation of quality indicators Preparation for external audits (MDK) Identification of improvement potential
Practical example: Before an upcoming MDK audit, the AI analyzes care documentation, identifies possible weaknesses, and creates a checklist for preparation.
4. Transfer Management When transferring residents between different facilities:
Automated creation of transfer forms
Summarization of relevant information from care documentation
Ensuring completeness of all required information
Seamless information transfer between care facilities
Practical example: When transferring a resident to the hospital, the AI creates a complete transfer form with all relevant information on care needs, medication, and special requirements.
Safe Implementation of AI in Nursing Homes
Data Protection and Security
Handling sensitive health data requires special care: GDPR compliance: All AI solutions must meet strict data protection requirements Evaluating hosting options: Cloud solutions vs. on-premise systems Access rights: Granular permission concepts for different user groups Encryption: Secure transmission and storage of all data
Ethical Aspects
The use of AI in care raises ethical questions:
Human control: AI should support caregivers, not replace them
Transparency: Traceability of AI recommendations
Resident autonomy: Self-determination and dignity must be preserved
Justice: Equal access to AI-supported care
Introduction step-by-step
Dr. Manfred Criegee-Rieck, CIO of Nuremberg Hospital, recommends a pragmatic approach that can also be applied to nursing homes:
Determine hosting option: Cloud or own server?
Create technical foundation: Infrastructure and interfaces
Develop use cases with teams: Involve nursing staff
Gradually connect more data sources: From simple to complex
Prioritization by AI Maturity
Not every use case is suitable for getting started. A sensible sequence is:
Knowledge management: Finding and structuring information
Administration: Documentation, applications, transfer forms
Automation: Appointment scheduling, resource management
Decision support: Risk analyses, care planning
Challenges and Solutions
Staff Acceptance
The introduction of AI systems may face skepticism:
Early involvement: Include caregivers in planning from the beginning.
Start with a small team.
Training: Understandable introduction to AI functionality for all employees
Benefit orientation: Demonstrate concrete advantages for daily work
Feedback culture: Continuous improvement based on feedback
Technical Integration
Connecting to existing systems can be challenging:
Interfaces: Standardized APIs for data exchange
Modular structure: Gradual integration of individual functions
Pilot projects: Test phase with limited scope
Support: Technical assistance during implementation
Conclusion: AI as Support, Not Replacement
Artificial intelligence offers promising possibilities to improve care in homes and relieve staff. The key to success lies in a pragmatic, step-by-step approach focusing on concrete use cases that bring immediate benefits. The principle should always be: AI is a tool to support caregivers, not to replace them. Human attention and care remain the heart of good nursing – AI can merely help ensure that more time is available for this. With a well-thought-out implementation concept that considers data protection, ethics, and the needs of all involved, AI can already make a valuable contribution to meeting the challenges in care today.
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